Referências
ABDIN, M. et al. Phi-3 Technical Report: A Highly Capable
Language Model Locally on Your Phone., 2024. Disponível em:
<https://arxiv.org/abs/2404.14219>
ABONIZIO, H. Q. et al. Language-Independent Fake News
Detection: English, Portuguese, and Spanish Mutual Features.
Future Internet, v. 12, n. 5, 2020.
ABREU, S. C. DE; VIEIRA, R. Relp: Portuguese open relation extraction.
KO KNOWLEDGE ORGANIZATION, v. 44, n. 3, p. 163–177,
2017.
ACKEL, A. Abordagens digitais para estudos de Paleografia: desafios,
atualidade, desdobramentos.
LaborHistórico, v. 7, n. 3, p. 100–120,
2021.
AEJAS, B.; BELHI, A.; BOURAS, A. Smart Contracts
Auto-generation for Supply Chain Contexts. (F. Noël et al.,
Eds.)Product Lifecycle Management. PLM in Transition Times: The Place of
Humans and Transformative Technologies. Anais...Cham:
Springer Nature Switzerland, 2023.
AGICHTEIN, E.; GRAVANO, L. Snowball: Extracting relations from
large plain-text collections. Proceedings of the fifth ACM
conference on Digital libraries. Anais...2000.
AGNOLONI, T. et al. Making Italian Parliamentary
Records Machine-Actionable: the Construction of the
ParlaMint-IT corpus.
Proceedings of the Workshop ParlaCLARIN III within the 13th Language
Resources and Evaluation Conference. Anais...Marseille,
France: European Language Resources Association, jun. 2022. Disponível
em: <https://aclanthology.org/2022.parlaclarin-1.17>
AHA, D. W.; KIBLER, D.; ALBERT, M. K. Instance-based learning
algorithms. Machine Learning, v. 6, n. 1, p. 37–66,
1 jan. 1991.
AI@META. Llama
3 Model Card. 2024.
AJAY, H. B.; TILLET, P.; PAGE, E. B. Analysis of essays by
computer (AEC-II). Storrs, CT: Univeristy of
Connecticut, 1973.
AKBIK, A. et al. Multilingual information extraction with
PolyglotIE. Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: System Demonstrations.
Anais...2016. Disponível em: <https://aclanthology.org/C16-2056/>
ALBUQUERQUE, G. et al. Applying event classification to reveal the
Estado da Índia. Proceedings of the International Conference on
the Computational treatment of Portuguese, PROPOR, a2024.
ALBUQUERQUE, H. et al. UlyssesNERQ:
Expanding Queries from Brazilian Portuguese
Legislative Documents through Named Entity Recognition. (P.
Gamallo et al., Eds.)Proceedings of the 16th International Conference on
Computational Processing of Portuguese - Vol. 1.
Anais...Santiago de Compostela, Galicia/Spain:
Association for Computational Lingustics, mar. b2024. Disponível em:
<https://aclanthology.org/2024.propor-1.35>
ALBUQUERQUE, H. O. et al. UlyssesNER-Br: A Corpus of Brazilian
Legislative Documents for Named Entity Recognition. (V.
Pinheiro et al., Eds.)Computational Processing of the Portuguese
Language. Anais...Cham: Springer International
Publishing, 2022. Disponível em: <https://github.com/ulysses-camara/>
ALBUQUERQUE, H. O. et al. Named entity recognition: a
survey for the portuguese language. Procesamiento del
Lenguaje Natural, a2023.
ALBUQUERQUE, H. O. et al. On the Assessment of
Deep Learning Models for Named Entity Recognition of Brazilian Legal
Documents. (N. Moniz et al., Eds.)Progress in Artificial
Intelligence. Anais...Cham: Springer Nature
Switzerland, b2023.
ALEIXO, P.; PARDO, T. A. S. Uma Ferramenta Semi-automática para
Anotação de Córpus pela Teoria Discursiva Multidocumento CST.
[s.l.] Instituto de Ciências Matemáticas e de Computação, 2008.
ALIGULIYEV, R. M. et al. COSUM: Text summarization based on clustering
and optimization. Expert Systems, v. 36, n. 1, p.
e12340, 2019.
ALIKANIOTIS, D.; YANNAKOUDAKIS, H.; REI, M. Automatic Text
Scoring Using Neural Networks. Proceedings of the 54th Annual
Meeting of the Association for Computational Linguistics.
Anais...Association for Computational Linguistics,
2016.
ALLES, V. J. Construção de um
corpus para extrair entidades nomeadas do Diário Oficial da
União utilizando aprendizado supervisionado.
mathesis—[s.l.] Master’s thesis, Universidade Federal de
Brasília, 2018.
ALMEIDA, G. DE. Translating the post-editor: an investigation of
post-editing changes and correlations with professional experience
across two Romance languages. 2013. Disponível em: <https://api.semanticscholar.org/CorpusID:60255248>
ALMEIDA, P. G. R. Uma
jornada para um Parlamento inteligente: Câmara dos Deputados do
Brasil. Red Información, v. 24, 2021.
ALMOUZINI, S.; KHEMAKHEM, M.; ALAGEEL, A. Detecting Arabic
Depressed Users from Twitter Data. Procedia Computer
Science, v. 163, p. 257–265, 2019.
ALONSO, M. A. et al. Sentiment Analysis
for Fake News Detection. Electronics, v. 10, n. 11,
2021.
ALUÍSIO, S. M. et al. Detecting Respiratory Insufficiency Via Voice
Analysis: The SPIRA Project. Em: Practical Machine
Learning for Developing Countries on the Tenth International Conference
on Learning Representations. Proceeding. [s.l.] ICLR, 2022.
ALUÍSIO, S.; GASPERIN, C. Fostering Digital Inclusion and
Accessibility: The PorSimples project for
Simplification of Portuguese Texts. (T. Solorio,
T. Pedersen, Eds.)Proceedings of the NAACL HLT
2010 Young Investigators Workshop on Computational Approaches to
Languages of the Americas. Anais...Los
Angeles, California: Association for Computational Linguistics, jun.
2010. Disponível em: <https://aclanthology.org/W10-1607>
ALVARES, R. V.; GARCIA, A. C. B.; FERRAZ, I. STEMBR: A stemming
algorithm for the Brazilian Portuguese language. Portuguese
conference on artificial intelligence.
Anais...Springer, 2005.
AMARAL, D.; VIEIRA, R. Nerp-crf: uma ferramenta para o reconhecimento de
entidades nomeadas por meio de conditional random fields.
Linguamática (Braga), 2014.
AMERICAN PSYCHIATRIC ASSOCIATION. Diagnostic and
Statistical Manual of Mental Disorders 5th edition.
Arlington, VA: American Psychiatric Association, 2013.
AMORIM, E.; CANÇADO, M.; VELOSO, A. Automated Essay Scoring in
the Presence of Biased Ratings. Proceedings of the 2018
Conference of the North American Chapter of the Association
for Computational Linguistics: Human Language Technologies.
Anais...Association for Computational Linguistics,
2018.
AMORIM, E.; VELOSO, A. A Multi-aspect Analysis of Automatic
Essay Scoring for Brazilian
Portuguese. Proceedings of the Student Research
Workshop at the 15th Conference of the European Chapter of
the Association for Computational Linguistics.
Anais...Valencia, Spain: Association for Computational
Linguistics, abr. 2017.
ANANIADOU, S.; MCNAUGHT, J. Text Mining for Biology And
Biomedicine. Norwood, MA, USA: Artech House, Inc., 2005.
ANCHIÊTA, R. T. et al. PiLN IDPT 2021: Irony
Detection in Portuguese Texts with Superficial Features and
Embeddings. Proceedings of the Iberian Languages Evaluation
Forum (IberLEF 2021) co-located with the Conference of the Spanish
Society for Natural Language Processing (SEPLN 2021),
XXXVII International Conference of the Spanish Society for
Natural Language Processing., Málaga, Spain, September,
2021. Anais...2021.
ANDERSEN, P. M. et al. Automatic extraction of facts from press
releases to generate news stories. Third Conference on Applied
Natural Language Processing. Anais...1992.
ANDREW, J. J.; TANNIER, X. Automatic Extraction of
Entities and Relation from Legal Documents. Proceedings of
the Seventh Named Entities Workshop. Anais...Melbourne,
Australia: Association for Computational Linguistics, jul. 2018.
ANGELIDIS, I.; CHALKIDIS, I.; KOUBARAKIS, M. Named entity
recognition, linking and generation for greek legislation.
Legal Knowledge and Information Systems. Anais...IOS
Press, 2018.
ANSARI, L.; JI, S. Ensemble hybrid learning methods for automated
depression detection. IEEE Transactions on computational Social
Systems, 2022.
ARAGÓN, M. E. et al. Detecting Depression in
Social Media using Fine-Grained Emotions.
NAACL-2019. Anais...Minneapolis,
USA: Association for Computational Linguistics, 2019.
ARAUJO, P. H. L. DE et al. LeNER-Br: A Dataset for Named Entity
Recognition in Brazilian Legal Text. (A. Villavicencio et al.,
Eds.)Computational Processing of the Portuguese Language.
Anais...Cham: Springer International Publishing, 2018.
Disponível em: <https://github.com/peluz/lener-br>
ARDILA, R. et al. Common Voice: A Massively-Multilingual Speech
Corpus. (N. Calzolari et al., Eds.)Proceedings of the Twelfth
Language Resources and Evaluation Conference.
Anais...Marseille, France: European Language Resources
Association, 2020. Disponível em: <https://aclanthology.org/2020.lrec-1.520/>
AREVALO, E. M.; FONTEYN, L. MacBERTh: Development and Evaluation
of a Historically Pre-trained Language Model for English
(1450-1950). ICON Workshop on Natural Language Processing for
Digital Humanities. Anais...2021.
ARFÉ, B.; MASON, L.; FAJARDO, I. Simplifying informational text
structure for struggling readers. Read Writ (2018) Volume 31,
Issue 9, p. 2191–2210, 2018.
ARORA, A. K. A. A. S. A. A. Anxious Depression Prediction in
Real-time Social Data. International Conference on Advances in
Engineering Science Management & Technology.
Anais...Dehradun, India: 2019.
ASAHARA, M.; MATSUMOTO, Y. Japanese named entity extraction with
redundant morphological analysis. Proceedings of the 2003 human
language technology conference of the North American chapter of the
association for computational linguistics.
Anais...2003.
ASCHBRENNER, K. A. et al. A survey of online and mobile technology use
at peer support agencies. Psychiatric Quarterly, p.
1–10, 2018.
ASSI, F. M. et al. UFSCar’s Team at ABSAPT 2022:
Using Syntax, Semantics and Context for Solving the Tasks.
Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)
co-located with the Conference of the Spanish Society for Natural
Language Processing (SEPLN 2022), A
Coruña, Spain, September 20, 2022.
Anais...2022.
AVANÇO, L. V.; NUNES, M. DAS G. V. Lexicon-Based Sentiment
Analysis for Reviews of Products in Brazilian Portuguese.
Proceedings of the 2014 Brazilian Conference on Intelligent Systems.
Anais...2014.
AVERINA, M.; LEVANOVA, O.; KASATKINA, N. Named Entity
Recognition for Russian Judicial Rulings Text. 2022 32nd
Conference of Open Innovations Association (FRUCT).
Anais...2022.
AZIZ, W.; SPECIA, L. Fully Automatic Compilation of a
Portuguese-English Parallel Corpus for Statistical Machine
Translation. STIL 2011. Anais...Cuiabá, MT:
2011.
AZZIMONTI, M.; FERNANDES, M. Social media
networks, fake news, and polarization. European Journal of
Political Economy, v. 76, p. 102256, 2023.
BAADE, A.; PENG, P.; HARWATH, D. MAE-AST: Masked
Autoencoding Audio Spectrogram Transformer. (H. Ko, J. H. L.
Hansen, Eds.)Interspeech 2022, 23rd Annual Conference of the
International Speech Communication Association, Incheon, Korea, 18-22
September 2022. Anais...ISCA, 2022.
Disponível em: <https://doi.org/10.21437/Interspeech.2022-10961>
BABU, A. et al. XLS-R: Self-supervised cross-lingual speech
representation learning at scale. arXiv preprint
arXiv:2111.09296, 2021.
BACH, N. X. et al. Reference Extraction from
Vietnamese Legal Documents. Proceedings of the 10th
International Symposium on Information and Communication Technology.
Anais...: SoICT ’19.New York, NY, USA: Association for
Computing Machinery, 2019.
BAEVSKI, A. et al. wav2vec 2.0: A Framework for Self-Supervised
Learning of Speech Representations., 2020. Disponível em:
<https://arxiv.org/abs/2006.11477>
BAEZA-YATES, R. A.; RIBEIRO-NETO, B. A. Modern Information Retrieval-the
concepts and technology behind search. 2011.
BAEZA-YATES, R.; RIBEIRO-NETO, B.
Recuperação de
Informação-: Conceitos e Tecnologia das
Máquinas de Busca. [s.l.] Bookman Editora, 2013.
BAHDANAU, D.; CHO, K.; BENGIO, Y. Neural Machine Translation by
Jointly Learning to Align and Translate. (Y. Bengio, Y. LeCun,
Eds.)3rd International Conference on Learning Representations,
ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference
Track Proceedings. Anais...San Diego, California.:
2015. Disponível em: <http://arxiv.org/abs/1409.0473>
BALAGE FILHO, P. P.; PARDO, T. A. S.; ALUÍSIO, S. M. An
Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment
Analysis. Proceedings of the 9th Brazilian Symposium in
Information and Human Language Technology.
Anais...2013.
BALAGE FILHO, P. P.; PARDO, T. A. S.; NUNES, M. DAS G. V.
Sumarização automática de textos científicos: Estudo de caso com
o sistema gistsumm. [s.l.] Instituto de Ciências Matemáticas e
de Computação da Universidade de São Paulo, 2007.
BALAMURALI, B. T. et al. Deep Neural Network-Based
Respiratory Pathology Classification Using Cough Sounds.
Sensors, v. 21, n. 16, 2021.
BANARESCU, L. et al. Abstract Meaning
Representation for Sembanking. Proceedings of the
7th Linguistic Annotation Workshop and Interoperability with Discourse.
Anais...Sofia, Bulgaria: Association for Computational
Linguistics, 2013. Disponível em: <http://aclweb.org/anthology/W13-2322>
BANERJEE, S.; LAVIE, A. METEOR: An Automatic Metric
for MT Evaluation with Improved Correlation with Human
Judgments. (J. Goldstein et al., Eds.)Proceedings of the
ACL Workshop on Intrinsic and Extrinsic Evaluation Measures
for Machine Translation and/or Summarization.
Anais...Ann Arbor, Michigan: Association for
Computational Linguistics, jun. 2005. Disponível em: <https://aclanthology.org/W05-0909>
BANKO, M. et al. Open Information Extraction from the
Web. Proceedings of the 20th International Joint Conference on
Artifical Intelligence. Anais...: IJCAI’07.San
Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2007. Disponível
em: <http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=9909B5C03DA1A3CCFFF4263898B69100?doi=10.1.1.74.5174&rep=rep1&type=pdf>
BANZA, A. P. A edição digital da História do Futuro, de António
Vieira: arquivo e ferramentas. Actas da Jornada de Humanidades
Digitais do CIDEHUS (to appear). Anais...2022.
BARACHO, J.; LISBOA, L.; LOPES, R. Levantamento e Análise
Qualitativa de Bases de Dados de Fake News em Português. Anais
do VI Workshop sobre as Implicações da Computação na Sociedade.
Anais...Porto Alegre, RS, Brasil: SBC, 2025. Disponível
em: <https://sol.sbc.org.br/index.php/wics/article/view/35944>
BARBOSA DE LIMA, T. et al. Avaliação Automática de
Redação: Uma revisão sistemática. Revista Brasileira de
Informática na Educação, v. 31, p. 205–221, 2023.
BARBOSA, G. C. G.; GLAUBER, R.; CLARO, D. B. Classificação de
Relações Abertas Utilizando Features Independentes do Idioma.
Proceedings of the 4th Symposium on Knowledge Discovery, Mining and
Learning (KDMiLe). Anais...SBC, 2016.
BARRAULT, L. et al. Findings of the 2019 Conference on
Machine Translation (WMT19). Proceedings of
WMT. Anais...Florence, Italy: 2019.
BARRAULT, L. et al. Findings of the 2020 Conference on Machine
Translation (WMT20). Proceedings of the Fifth
Conference on Machine Translation. Anais...Online:
Association for Computational Linguistics, nov. 2020. Disponível em:
<https://www.aclweb.org/anthology/2020.wmt-1.1>
BARRETT, M. J.; AGIC, Z.; SØGAARD, A. The Dundee Treebank.
Proceedings of the Fourteenth International Workshop on
Treebanks and Linguistic Theories: TLT14, p. 242–248, 2015.
BARRIERE, V.; FOURET, A. May I Check Again?
— A simple but efficient way to generate and use contextual
dictionaries for Named Entity Recognition. Application to
French Legal Texts. Proceedings of the 22nd Nordic
Conference on Computational Linguistics. Anais...Turku,
Finland: Linköping University Electronic Press, 2019.
Disponível em: <https://aclanthology.org/W19-6136>
BARROS, T. S. Um modelo BERT para sumarização extrativa de
textos em documentos da Polícia Federal. mathesis—[s.l.]
(Mestrado em Ciências da Computação) - Programa de Pós-Graduação em
Ciência da Computação da Universidade Federal de Campina Grande, 2022.
BARZILAY, R.; ELHADAD, N.; MCKEOWN, K. Sentence ordering in
multidocument summarization. Proceedings of the first
international conference on Human language technology research.
Anais...2001.
BASILE, V. et al. We Need to Consider Disagreement in
Evaluation. (K. Church, M. Liberman, V. Kordoni,
Eds.)Proceedings of the 1st Workshop on Benchmarking: Past, Present and
Future. Anais...Online: Association for Computational
Linguistics, ago. 2021. Disponível em: <https://aclanthology.org/2021.bppf-1.3/>
BATISTA, H. H. et al. A comparative analysis
of text embedding approach to extract named entities in Portuguese legal
documents. Anais do XVIII Encontro Nacional de
Inteligência Artificial e Computacional.
Anais...SBC, 2021.
BAXENDALE, P. B. Machine-made index for technical literature—an
experiment. IBM Journal of research and development, v.
2, n. 4, p. 354–361, 1958.
BELCAVELLO, F. et al. Frame-Based Annotation of Multimodal
Corpora: Tracking (A)Synchronies in Meaning Construction. (T.
T. Torrent et al., Eds.)Proceedings of the International FrameNet
Workshop 2020: Towards a Global, Multilingual FrameNet.
Anais...Marseille, France: European Language Resources
Association, 2020. Disponível em: <https://aclanthology.org/2020.framenet-1.4>
BELCAVELLO, F. FrameNet Annotation for Multimodal Corpora:
devising a methodology for the semantic representation of text-image
interactions in audiovisual productions. Tese de Doutorado em
Linguística—Juiz de Fora: Universidade Federal de Juiz de Fora, 2023.
BELCAVELLO, F. et al. Frame2: A
FrameNet-based Multimodal Dataset for Tackling
Text-image Interactions in Video. (N. Calzolari et al.,
Eds.)Proceedings of the 2024 Joint International Conference on
Computational Linguistics, Language Resources and Evaluation
(LREC-COLING 2024). Anais...Torino, Italia: ELRA; ICCL,
2024. Disponível em: <https://aclanthology.org/2024.lrec-main.655/>
BENDER, E. M. Linguistically Naïve != Language
Independent: Why NLP Needs Linguistic Typology.
Proceedings of the EACL 2009 Workshop on the Interaction
between Linguistics and Computational Linguistics: Virtuous, Vicious or
Vacuous? Anais...Athens, Greece: Association for
Computational Linguistics, mar. 2009. Disponível em: <https://www.aclweb.org/anthology/W09-0106>
BENGIO, Y.; COURVILLE, A.; VINCENT, P. Representation learning: A review
and new perspectives. IEEE transactions on pattern analysis and
machine intelligence, v. 35, n. 8, p. 1798–1828, 2013.
BERBER SARDINHA, T. Linguística de Corpus. Barueri:
Manole, 2004.
BERTI, L. C. et al. Fundamental frequency related parameters in
Brazilians with COVID-19. The Journal of the Acoustical Society
of America, v. 153, n. 1, p. 576–585, 2023.
BERTI, L. C. et al. Acoustic Characteristics of Voice and Speech
in Post-COVID-19. Healthcare. Anais...MDPI,
2025.
BHARDWAJ, S.; AGGARWAL, S.; MAUSAM, M. CaRB: A crowdsourced
benchmark for open IE. Proceedings of the 2019 Conference on
Empirical Methods in Natural Language Processing and the 9th
International Joint Conference on Natural Language Processing
(EMNLP-IJCNLP). Anais...2019.
BHATTACHARYA, D. et al. Coswara: A respiratory sounds and symptoms
dataset for remote screening of SARS-CoV-2 infection. Scientific
data, v. 10, n. 1, p. 397, 2023.
BIBER, D. Representativeness in Corpus
Design. Literary and Linguistic Computing, v. 8, n.
4, p. 243–257, jan. 1993.
BIBER, D. Register:
Overview. Em: BROWN, K. (Ed.). Encyclopedia of Language
& Linguistics (Second Edition). Second Edition ed. Oxford:
Elsevier, 2006. p. 476–482.
BICK, E. The Parsing
System "Palavras": Automatic Grammatical Analysis of Portuguese in a
Constraint Grammar Framework. tese de doutorado—[s.l.]
Aarhus University Press, Denmark; University of Arhus, 2000.
BIKEL, D. M.; SCHWARTZ, R.; WEISCHEDEL, R. M. An algorithm that learns
what’s in a name. Machine learning, v. 34, p. 211–231,
1999.
BIRD, S. NLTK: the natural language toolkit.
Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions.
Anais...2006.
BITTENCOURT JR., J. A. S. Avaliação
automática de redação em língua
portuguesa empregando redes neurais profundas. mathesis—[s.l.]
Universidade Federal de Goiás, 2020.
BLEI, D. M.; MORENO, P. J. Topic Segmentation with an Aspect
Hidden Markov Model. Proceedings of the 24th Annual
International ACM SIGIR Conference on Research
and Development in Information Retrieval. Anais...New
York, NY, USA: Association for Computing Machinery, 2001.
BOITO, M. Z. Simplificação lexical de substantivos e multiword
expressions. Salão de Iniciação Científica (26. : 2014 out.
20-24 : UFRGS, Porto Alegre, RS), 2014.
BOJANOWSKI, P. et al. Enriching Word Vectors with Subword Information.
Transactions of the Association for Computational
Linguistics, v. 5, p. 135–146, 2017.
BOJAR, O. et al. Findings of the 2016 Conference on
Machine Translation. Proceedings of the First Conference
on Machine Translation. Anais...Berlin, Germany:
Association for Computational Linguistics, 2016.
BOND JR., C. F.; DEPAULO, B. M. Accuracy of Deception Judgments.
Personality and Social Psychology Review, v. 10, n. 3,
p. 214–234, 2006.
BONIFACIO, L. H. et al. A Study on the
Impact of Intradomain Finetuning of Deep Language Models for Legal Named
Entity Recognition in Portuguese. (R. Cerri, R. C. Prati,
Eds.)Intelligent Systems. Anais...Cham: Springer
International Publishing, 2020.
BONIFACIO, L. H. et al. mMARCO: A Multilingual Version of MS
MARCO Passage Ranking Dataset., 2021. Disponível em: <https://arxiv.org/abs/2108.13897>
BONIFACIO, L. H. N. Modelos
Profundos de Linguagem para Reconhecimento de Entidades Nomeadas em
Domínio Jurídico. mathesis—[s.l.] Master’s thesis,
Universidade Federal de Mato Grosso do Sul, 2020.
BORDINO, I. et al. Garnlp:
a natural language processing pipeline for garnishment documents.
Information Systems Frontiers, v. 23, p. 101–114, 2021.
BORIN, E.; DONATO, F. Financial Sustainability of Digitizing Cultural
Heritage: The International Platform Europeana. Journal of Risk
and Financial Management, v. 16, n. 10, p. 421, 2023.
BOWKER, L.; PEARSON, J. Working with Specialized
Language: A Practical Guide to Using Corpora. 1. ed.
London: Routledge, 2002.
BRANDT, M. B. Modelagem
da informação legislativa: arquitetura da
informação para o processo legislativo
brasileiro. tese de doutorado—[s.l.] Universidade Estadual
Paulista (Unesp), 2020.
BRAUN, H. I. Understanding Scoring Reliability: Experiments in
Calibrating Essay Readers. Journal of Educational
Statistics, v. 13, n. 1, p. 1–18, 1988.
BREITFELLER, L. et al. Finding Microaggressions in the Wild: A
Case for Locating Elusive Phenomena in Social Media Posts.
Proceedings of the 2019 Conference on Empirical Methods in Natural
Language Processing and the 9th International Joint Conference on
Natural Language Processing (EMNLP-IJCNLP).
Anais...2019.
BRICIU, A.; LUPEA, M. Studying the language
of mental illness in Romanian social media.
IEEE 14th International Conference on Intelligent Computer
Communication and Processing (ICCP). Anais...2018.
BRIDGEMAN, B. Handbook of automated essay evaluation: Current
applications and new directions. Em: SHERMIS, M. D.; BURSTEIN, J.
(Eds.). [s.l.] Routledge/Taylor & Francis Group, 2013. p. 221–232.
BRIGHAM, E. O.; MORROW, R. The fast Fourier transform. IEEE
spectrum, v. 4, n. 12, p. 63–70, 1967.
BRIN, S. Extracting patterns and relations from the world wide
web. International workshop on the world wide web and
databases. Anais...Springer, 1998.
BRITO, M. et al. CDJUR-BR - Uma Coleção Dourada do Judiciário
Brasileiro com Entidades Nomeadas Refinadas. Anais do XIV
Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana.
Anais...Porto Alegre, RS, Brasil: SBC, 2023. Disponível
em: <https://github.com/mauriciobritojr/CDJUR-BR>
BRITTO, H.; FINGER, M.; GALVES, C. Computational and linguistic aspects
of the construction of The Tycho Brahe Parsed Corpus of Historical
Portuguese. Romanistische Korpuslinguistik, Korpora und
gesprochene Sprache, Romance Corpus Linguistics, Corpora and Spoken
Language, ScriptOralia, v. 126., 2002.
BROWN, C. et al. Exploring automatic diagnosis of COVID-19 from
crowdsourced respiratory sound data. Proceedings of the 26th
ACM SIGKDD international conference on knowledge discovery & data
mining. Anais...a2020.
BROWN, P. et al. A statistical approach to language
translation. Proceedings of the 12th conference on
Computational linguistics -.
Anais...Budapest, Hungry: Association for Computational
Linguistics, 1988. Disponível em: <http://portal.acm.org/citation.cfm?doid=991635.991651>.
Acesso em: 10 jun. 2020
BROWN, T. B. et al. Language Models are Few-Shot
Learners. (H. Larochelle et al., Eds.)Advances in Neural
Information Processing Systems. Anais...Curran
Associates, Inc., b2020. Disponível em: <https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html>
BRUM, H.; NUNES, M. DAS G. V. Building a Sentiment Corpus
of Tweets in Brazilian Portuguese. (N. C. (Conference
chair) et al., Eds.)Proceedings of the Eleventh International Conference
on Language Resources and Evaluation (LREC 2018).
Anais...Miyazaki, Japan: European Language Resources
Association (ELRA), mar. 2018.
BRUNETTE, M. et al. Use of smartphones, computers and social media among
people with SMI: opportunity for intervention. Community Mental
Health Journal, p. 1–6, 2019.
BUCCI, S.; SCHWANNAUER, M.; BERRY, N. The digital revolution and its
impact on mental health care. Psychology and Psychotherapy:
Theory, Research and Practice, v. 92, n. 2, p. 277–297, 2019.
BUCKLEY, C.; VOORHEES, E. M. Evaluating evaluation measure
stability. ACM SIGIR Forum. Anais...ACM New
York, NY, USA, 2017.
BUENO, R. O. et al. Overview of the Task on Irony Detection in
Spanish Variants. Proceedings of the Iberian Languages
Evaluation Forum co-located with 35th Conference of the Spanish Society
for Natural Language Processing. Anais...2019.
BURDISSO, S. G.; ERRECALDE, M.; MONTES-Y-GÓMEZ, M. t-SS3: a text
classifier with dynamic n-grams for early risk detection over text
streams. Pattern Recognition Letters, v.
138, p. 130–137, 2020.
BURRISS, L. L. Attribution in network radio news: A cross-network
analysis. Journalism Quarterly, v. 65, n. 3, p.
690–694, 1988.
BURSTEIN, J. Opportunities for Natural Language Processing
Research in Education. (A. Gelbukh, Ed.)Computational
Linguistics and Intelligent Text Processing.
Anais...Berlin, Heidelberg: Springer Berlin Heidelberg,
2009.
CABRAL, B.; SOUZA, M.; CLARO, D. B. PortNOIE: A Neural Framework
for Open Information Extraction for the Portuguese Language.
International Conference on Computational Processing of the Portuguese
Language. Anais...Springer, 2022.
CABRAL, L. et al. FakeWhastApp.BR:
NLP and Machine Learning Techniques for Misinformation
Detection in Brazilian Portuguese WhatsApp Messages.
Proceedings of the 23rd International Conference on Enterprise
Information Systems (ICEIS 2021) - Volume 1.
Anais...2021.
CABRÉ, M. T. A Terminologia, uma disciplina em
evolução: passado, presente e alguns elementos
de futuro. Debate Terminológico. ISSN:
1813-1867, n. 01, 2005.
CABRERA-DIEGO, L. A.; GHEEWALA, A. Jus Mundi at
SemEval-2023 Task 6: Using a Frustratingly
Easy Domain Adaption for a Legal Named Entity Recognition
System. Proceedings of the 17th International Workshop on
Semantic Evaluation (SemEval-2023). Anais...Toronto,
Canada: Association for Computational Linguistics, jul. 2023.
CAI, Y. et al. Rank-Then-Score: Enhancing Large Language Models
for Automated Essay Scoring., 2025. Disponível em: <https://arxiv.org/abs/2504.05736>
CAMERON, H. F.; GONÇALVES, M. F.; QUARESMA, P. Linguistic and
orthographical classic Portuguese variants Challenges for
NLP. Proceedings of the 14th International
Conference on the Computational Processing of Portuguese.
Anais...2020.
CAMERON, H.; OLIVAL, F.; VIEIRA, R. Planear a normalização
automática: tipologia de variação gráfica do corpus das Memórias
Paroquiais (1758). LaborHistórico, v. 9, n. 1, p.
52234, 2023.
CANDIDO JUNIOR, A. Análise bidirecional da língua na
simplificação sintática em textos de português voltada à acessibilidade
digital. ICMC - USP São Carlos: Biblioteca Digital USP, 2013.
CANDIDO-JUNIOR, A.; OLIVEIRA, M. DE; ALUÍSIO, S. M. Simplifica: um
Sistema Web de Autoria de Textos Simplificados. Simpósio
Brasileiro de Sistemas Multimídia e Web (Webmedia 2009) v.2, p.
55–58, 2009.
CARDELLINO, C. et al. Legal NERC with ontologies,
Wikipedia and curriculum learning. Proceedings of
the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 2, Short Papers.
Anais...Valencia, Spain: Association for Computational
Linguistics, abr. 2017. Disponível em: <https://aclanthology.org/E17-2041>
CARDOSO, P. C. F. et al. CSTNews-a discourse-annotated corpus
for single and multi-document summarization of news texts in Brazilian
Portuguese. Proceedings of the 3rd RST Brazilian Meeting.
Anais...2011.
CARDOSO, P. C. F. Exploração de métodos de sumarização
automática multidocumento com base em conhecimento
semântico-discursivo. tese de doutorado—[s.l.] (Doutorado em
Ciências de Computação e Matemática Computacional) - Instituto de
Ciências Matemáticas e de Computação, Universidade de São Paulo, 2014.
CARL, M.; WAY, A. (EDS.). Recent
Advances in Example-Based
Machine Translation. [s.l.]
Springer Netherlands, 2003.
CARMO, D. et al. PTT5: Pretraining
and validating the T5 model on Brazilian Portuguese
data. CoRR, v. abs/2008.09144, 2020.
CARNEIRO, F. C. A. D. F. A. F. J. N. A. V. Early Detection of Depression:
Social Network Analysis and Random Forest Techniques. J Med
Internet Res, v. 21, n. 6, p. e12554, 2019.
CARPINETO, C.; ROMANO, G. A survey of automatic query expansion in
information retrieval. Acm Computing Surveys (CSUR), v.
44, n. 1, p. 1–50, 2012.
CARROLL, J. et al. Practical Simplification of English Newspaper
Text to Assist Aphasic Readers. In Proc. of AAAI-98 Workshop on
Integrating Artificial Intelligence and Assistive Technology.
Anais...1998.
CARVALHO, F.; SANTOS, G. DOS; GUEDES, G. P. AffectPT-br: an
Affective Lexicon based on LIWC 2015. Proceedings of the 37th
International Conference of the Chilean Computer Science Society.
Anais...2018.
CARVALHO, P. et al. Clues for Detecting Irony in User-Generated
Contents: Oh...!! It’s "so Easy" ;-). Proceedings of the 1st
International CIKM Workshop on Topic-Sentiment Analysis for Mass
Opinion. Anais...2009.
CARVALHO, P.; SILVA, M. J. SentiLex-PT 02.
https://b2share.eudat.eu, 2017. Disponível em: <https://b2share.eudat.eu/records/93ab120efdaa4662baec6adee8e7585f>
CASANOVA, E. et al. Transfer Learning and Data
Augmentation Techniques to the COVID-19 Identification Tasks in ComParE
2021. Proc. Interspeech 2021.
Anais...a2021.
CASANOVA, E. et al. Deep Learning against COVID-19:
Respiratory Insufficiency Detection in Brazilian
Portuguese Speech. Findings of the Association for
Computational Linguistics: ACL-IJCNLP 2021.
Anais...Online: Association for Computational
Linguistics, ago. b2021.
CASELI, H. DE M. et al. Building a Brazilian Portuguese parallel corpus
of original and simplified texts. Advances in Computational
Linguistics, Research in Computer Science (CICLing-2009), v.
41, p. 59–70, 2009.
CÁSSIA ALVES, V. DE et al. College
students-in-the-loop for their mental health: a case of AI and humans
working together to support well-being. Interaction Design
and Architecture(s), n. 59, p. 79–94, 2024.
CASTANO, A.; CASACUBERTA, F. A connectionist approach to machine
translation. 5th European Conference on Speech Communication
and Technology (Eurospeech 1997). Anais...ISCA, set.
1997. Disponível em: <http://dx.doi.org/10.21437/eurospeech.1997-50>
CASTILHO, S. et al. Does post-editing increase usability? A
study with Brazilian Portuguese as Target Language. Proceedings
of the 17th annual conference of the European association for machine
translation. Anais...2014.
CASTILHO, S. et al. A comparative quality evaluation of PBSMT
and NMT using professional translators. Proceedings of Machine
Translation Summit XVI: Research Track. Anais...a2017.
CASTILHO, S. et al. Is Neural
Machine Translation the New
State of the Art? The Prague
Bulletin of Mathematical Linguistics, v. 108, n. 1, p. 109–120,
jun. b2017.
CASTILHO, S. et al. Approaches to Human and Machine Translation
Quality Assessment. Em: Translation Quality
Assessment: From Principles to Practice. Machine
Translation: Technologies e Applications. [s.l.] Springer International
Publishing, 2018. v. 1p. 9–38.
CASTILHO, S. et al. Editors’ foreword to
the special issue on human factors in neural machine translation.
Machine Translation, v. 33, n. 1–2, p. 1–7, maio a2019.
CASTILHO, S. On the Same Page? Comparing IAA in
Sentence and Document Level Human MT Evaluation. Proceedings of
the Fifth Conference on Machine Translation.
Anais...Association for Computational Linguistics, nov.
2020. Disponível em: <https://www.aclweb.org/anthology/2020.wmt-1.137>
CASTILHO, S. Towards Document-Level Human MT
Evaluation: On the Issues of Annotator Agreement, Effort and
Misevaluation. Proceedings of the Workshop on Human Evaluation
of NLP Systems. Anais...Association for Computational
Linguistics, abr. 2021. Disponível em: <https://www.aclweb.org/anthology/2021.humeval-1.4>
CASTILHO, S. How Much Context Span is Enough? Examining
Context-Related Issues for Document-level MT. Proceedings of
the Language Resources and Evaluation Conference.
Anais...Marseille, France: European Language Resources
Association, 2022. Disponível em: <https://aclanthology.org/2022.lrec-1.323>
CASTILHO, S. et al. Translation Systems Care for Context? What
About a GPT Model? Proceedings of the 24th Annual Conference of
the European Association for Machine Translation.
Anais...Tampere, Finland: EAMT, 2023. Disponível em:
<https://events.tuni.fi/uploads/2023/06/11678752-proceedings-eamt2023.pdf>
CASTILHO, S.; RESENDE, N. Post-Editese in Literary Translations.
Information, v. 13, n. 2, p. 66, 2022.
CASTILHO, S.; RESENDE, N.; MITKOV, R. What Influences the
Features of Post-editese? A Preliminary Study. Proceedings of
the Human-Informed Translation and Interpreting Technology Workshop
(HiT-IT 2019). Anais...Varna, Bulgaria: Incoma Ltd.,
Shoumen, Bulgaria, set. b2019. Disponível em: <https://aclanthology.org/W19-8703>
CASTRO, P. Aprendizagem
profunda para reconhecimento de entidades nomeadas em domínio
jurídico. mathesis—[s.l.] Master’s thesis, Universidade
Federal de Goiás, 2019.
CASTRO, P. V. Q. DE; SILVA, N. F. F. DA; SOARES, A. DA S.
Portuguese Named Entity Recognition Using
LSTM-CRF. (A. Villavicencio et al.,
Eds.)Proceedings of the 13th International Conference on the
Computational Processing of the Portuguese Language.
Anais...2018.
ÇETINDAĞ, C.; YAZICIOĞLU, B.; KOÇ, A. Named-entity
recognition in Turkish legal texts. Natural Language
Engineering, p. 1–28, 2022.
CHAKRABORTY, A. et al. Stop Clickbait: Detecting and preventing
clickbaits in online news media. 2016 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining (ASONAM).
Anais...2016.
CHALKIDIS, I. et al. LEGAL-BERT:
The Muppets straight out of Law School. Findings of the
Association for Computational Linguistics: EMNLP 2020.
Anais...Online: Association for Computational
Linguistics, nov. 2020.
CHALKIDIS, I. et al. Regulatory
Compliance through Doc2Doc Information
Retrieval: A case study in EU/UK legislation
where text similarity has limitations. Proceedings of the
16th Conference of the European Chapter of the Association for
Computational Linguistics: Main Volume. Anais...Online:
Association for Computational Linguistics, abr. 2021.
CHALKIDIS, I.; ANDROUTSOPOULOS, I. A deep learning
approach to contract element extraction. Em: Legal knowledge
and information systems. [s.l.] IOS Press, 2017. p. 155–164.
CHALKIDIS, I.; ANDROUTSOPOULOS, I.; ALETRAS, N. Neural Legal Judgment
Prediction in English. Proceedings of the 57th
Annual Meeting of the Association for Computational Linguistics.
Anais...Association for Computational Linguistics,
2019.
CHALKIDIS, I.; ANDROUTSOPOULOS, I.; MICHOS, A. Extracting Contract
Elements. Proceedings of the 16th Edition of the
International Conference on Articial Intelligence and Law.
Anais...: ICAIL ’17.New York, NY, USA: Association for
Computing Machinery, 2017.
CHALL, J. S.; DALE, E. Readability revisited: the new Dale-Chall
readability formula. [s.l.] Brookline Books, 1995.
CHALMERS, D. J. Syntactic transformations on distributed
representations. Connectionist Natural Language Processing:
Readings from Connection Science, p. 46–55, 1992.
CHANDRASEKAR, R.; DORAN, C.; SRINIVAS, B. Motivations and methods for
text simplification. Proceedings of the 16th International
Conference on Computational Linguistics (COLING), p. 1041–1044,
1996.
CHARLES, A. C.; RUBACK, L.; OLIVEIRA, J. Fakepedia Corpus: A
Flexible Fake News Corpus in Portuguese. Computational
Processing of the Portuguese Language: 15th International Conference,
PROPOR 2022, Fortaleza, Brazil, March 21–23, 2022, Proceedings.
Anais...Berlin, Heidelberg: Springer-Verlag, 2022.
Disponível em: <https://doi.org/10.1007/978-3-030-98305-5_4>
CHAVARRO, J. et al. FakeTrueBR: Um corpus
brasileiro de notícias falsas. Anais da XVIII Escola Regional
de Banco de Dados. Anais...Porto Alegre, RS, Brasil:
SBC, 2023. Disponível em: <https://sol.sbc.org.br/index.php/erbd/article/view/24352>
CHEN, A.; CHEN, D. O. Simulation of a machine learning enabled learning
health system for risk prediction using synthetic patient data.
Scientific Reports, v. 12, n. 1, p. 17917, out. 2022.
CHEN, P. P. The
Entity-Relationship Model - Toward a Unified View of Data.
ACM Trans. Database Syst., v. 1, n. 1, p. 9–36, 1976.
CHEN, Y. et al. Joint Entity and
Relation Extraction for Legal Documents with Legal Feature
Enhancement. Proceedings of the 28th International
Conference on Computational Linguistics.
Anais...Barcelona, Spain (Online): International
Committee on Computational Linguistics, dez. 2020.
CHEN, Y.; CONROY, N. J.; RUBIN, V. L. Misleading Online Content:
Recognizing Clickbait as "False News". Proceedings of the 2015
ACM on Workshop on Multimodal Deception Detection.
Anais...: WMDD ’15.New York, NY, USA: Association for
Computing Machinery, 2015. Disponível em: <https://doi.org/10.1145/2823465.2823467>
CHOUDHURY, M. D. et al. Predicting Depression via Social
Media. International AAAI Conference on Web and Social
Media (ICWSM). Anais...AAAI, 2013.
CHRISMAN, L. Learning recursive distributed representations for holistic
computation. Connection Science, v. 3, n. 4, p.
345–366, 1991.
CIAMPAGLIA, G. L. et al. Computational fact checking from knowledge
networks. PloS one, v. 10, n. 6, p. e0128193, 2015.
CIGNARELLA, A. T. et al. Overview of the EVALITA
2018 Task on Irony Detection in Italian Tweets (IronITA).
Proceedings of the Sixth Evaluation Campaign of Natural Language
Processing and Speech Tools for Italian. Final Workshop
(EVALITA 2018) co-located with the Fifth Italian Conference
on Computational Linguistics (CLiC-it 2018).
Anais...2018.
CLARK, K. et al. ELECTRA: Pre-training Text
Encoders as Discriminators Rather Than Generators. 8th
International Conference on Learning Representations, ICLR
2020. Anais...Addis Ababa, Ethiopia: OpenReview.net,
abr. 2020. Disponível em: <https://openreview.net/forum?id=r1xMH1BtvB>
CLARKE, D. J. B. et al. FAIRshake: Toolkit to
Evaluate the FAIRness of Research Digital Resources. Cell
Systems, v. 9, n. 5, p. 417–421, 2019.
CLEM, S. Post-Truth and Vices Opposed to Truth. v. 37, n. 2, p. 97–116,
2017.
COELHO, G. et al. Information Extraction
in the Legal Domain: Traditional Supervised Learning vs.
ChatGPT. INSTICC; SciTePress, 2024.
COELLO, J. M. A.; JUNQUEIRA, B. A. Automatic Analysis of Facebook Posts
and Comments Written in Brazilian Portuguese. International
Journal for Innovation Education and Research, 2019.
COHAN, A. et al. SMHD: a Large-Scale Resource for
Exploring Online Language Usage for Multiple Mental Health
Conditions. COLING-2018.
Anais...Santa Fe, USA: Association for
Computational Linguistics, 2018.
COHEN, J. P. et al. Problems in the deployment of machine-learned models
in health care. Cmaj, v. 193, n. 35, p. E1391–E1394,
2021.
COLEMAN, M.; LIAU, T. L. A computer readability formula designed for
machine scoring. Journal of Applied Psychology, v. 60,
p. 283–284, 1975.
COLLOVINI, S. et al. IberLEF 2019 Portuguese Named Entity
Recognition and Relation Extraction Tasks.
Proceedings of the Iberian Languages Evaluation Forum co-located
with 35th Conference of the Spanish Society for Natural Language
Processing. Anais...2019. Disponível em: <http://ceur-ws.org/Vol-2421/NER\_Portuguese\_overview.pdf>
CONCEIÇÃO, M. C.; ZANOLA, M. T. Terminologia e
mediação linguı́stica:
métodos, práticas e atividades.
Universidade do Algarve Editora, 2020.
CONEGLIAN, C. S.; SANTAREM SEGUNDO, J. E. Europeana no
Linked Open Data: conceitos de Web Semântica na dimensão aplicada das
Humanidades Digitais. Encontros Bibli: revista eletrônica de
biblioteconomia e ciência da informação, v. 22, n. 48, p.
88–99, 2017.
CONNEAU, A. et al. Unsupervised cross-lingual representation
learning at scale. Proceedings of the 58th Annual Meeting of
the Association for Computational Linguistics.
Anais...2020.
CONROY, N. K.; RUBIN, V. L.; CHEN, Y. Automatic
deception detection: Methods for finding fake news.
Proceedings of the Association for Information Science and
Technology, v. 52, n. 1, p. 1–4, 2015.
CONSOLI, B. S. et al. Embeddings for Named Entity Recognition in
Geoscience Portuguese Literature. Proceedings of The 12th
Language Resources and Evaluation Conference.
Anais...2020.
CONSORTIUM, L. D. ACE (Automatic Content Extraction)
English Annotation Guidelines for Events. Version, n.
5.4.3, 2005.
COPPERSMITH, G. et al. CLPsych 2015 Shared Task:
Depression and PTSD on Twitter. Second Workshop on
Computational Linguistics and Clinical Psychology: From Linguistic
Signal to Clinical Reality. Anais...Denver,
USA: Association for Computational Linguistics, 2015.
CORDEIRO, P. R.; PINHEIRO, V. Um corpus de notıcias
falsas do twitter e verificaçao automática de
rumores em lıngua portuguesa. Proceedings of the
Symposium in Information and Human Language Technology.
Anais...2019.
CORNU, G. Linguistique juridique. [s.l: s.n.].
CORRÊA, U. B. Análise de sentimento baseada em aspectos usando
aprendizado profundo: uma proposta aplicada à língua
portuguesa. tese de doutorado—[s.l.] Universidade Federal de
Pelotas, 2021.
CORREIA, F. A. et al. Fine-grained legal
entity annotation: A case study on the Brazilian Supreme Court.
Information Processing & Management, v. 59, n. 1,
p. 102794, 2022.
CORTES, C.; VAPNIK, V. Support-Vector
Networks. Machine Learning, v. 20, n. 3, p.
273–297, set. 1995.
COSTA, A. et al. A
linguistically motivated taxonomy for Machine Translation error
analysis. Machine Translation, v. 29, n. 2, p.
127–161, 2015.
COSTA, A. D. A tradução por máquina enriquecida semanticamente
com frames e papéis qualia. Ph.D. thesis—Juiz de Fora:
Universidade Federal de Juiz de Fora, 2020.
COSTA, P. B. DA et al. BERTabaporu:
assessing a genre-specific language model for Portuguese
NLP. Recents Advances in Natural Language Processing
(RANLP-2023). Anais...Varna, Bulgaria:
2023.
COSTA, R. et al. Expanding UlyssesNER-Br Named
Entity Recognition Corpus with Informal User-Generated Text.
(G. Marreiros et al., Eds.)Progress in Artificial Intelligence.
Anais...Cham: Springer International Publishing, 2022.
Disponível em: <https://github.com/ulysses-camara/>
COUTINHO, I.; MARTINS, B. Transformer-based
models for ICD-10 coding of death certificates with Portuguese text.
Journal of Biomedical Informatics, v. 136, p. 104232,
2022.
COUTO, J. M. M.; REIS, J. C. S.; BENEVENUTO, F. Can computer network
attributes be useful for identifying low-credibility websites? A case
study in Brazil. Social Network Analysis and
Mining, v. 14, n. 1, p. 153, 2024.
COWIE, J. R. Automatic analysis of descriptive texts.
First Conference on Applied Natural Language Processing.
Anais...1983.
COWIE, J.; LEHNERT, W. Information extraction. Communications of
the ACM, v. 39, n. 1, p. 80–91, 1996.
COX, A. N. A. M., Simon J. D. AND Gonzalez-Beltran. Ten simple rules for
making a vocabulary FAIR. PLOS Computational
Biology, v. 17, n. 6, p. 1–15, jun. 2021.
CRESTANI, F. et al. “Is this document
relevant?… probably” a survey of probabilistic models in
information retrieval. ACM Computing Surveys
(CSUR), v. 30, n. 4, p. 528–552, 1998.
CROFT, W. Typology and
Universals. 2. ed. [s.l.] Cambridge University
Press, 2002.
CROFT, W. B.; METZLER, D.; STROHMAN, T. Search engines:
Information retrieval in practice. [s.l.] Addison-Wesley, 2010.
v. 520
CSIKSZENTMIHALYI, M. Flow: The Psychology of Optimal
Experience. [s.l.] Harper Perennial, 2008.
CUCCHIARELLI, A.; VELARDI, P. Unsupervised named entity recognition
using syntactic and semantic contextual evidence. Computational
Linguistics, v. 27, n. 1, p. 123–131, 2001.
CUI, H. et al. Probabilistic query expansion using query
logs. Proceedings of the 11th international conference on World
Wide Web. Anais...2002.
CUI, L.; WEI, F.; ZHOU, M. Neural Open Information
Extraction. Proceedings of the 56th Annual Meeting of the
Association for Computational Linguistics (Volume 2: Short Papers).
Anais...2018.
CULOTTA, A.; MCCALLUM, A.; BETZ, J. Integrating probabilistic
extraction models and data mining to discover relations and patterns in
text. Proceedings of the Human Language Technology Conference
of the NAACL, Main Conference. Anais...2006.
CUNHA, A. L. V. DA. Coh-Metrix-Dementia: análise automática de
distúrbios de linguagem nas demências utilizando Processamento de
Línguas Naturais. ICMC - USP São Carlos: Biblioteca Digital
USP, 2015.
CUNHA, L. C. C. DA. Um Corpus anotado de mensagens do WhatsApp
em PT-BR para detecção automática de desinformação textual. https://github.com/cabrau/FakeWhatsApp.Br, 2021.
DA SILVA JR., J. A. Um avaliador automático de
redações. mathesis—[s.l.]
Universidade Federal do Espírito Santo, 2021.
DADICO, C. M. O Ódio Ancestral Como Elemento Constitutivo Do
Estado Moderno e Seus Reflexos Na Compreensão dos Crimes De Ódio: Um
Diálogo Entre o Direito Internacional e o Direito Brasileiro.
tese de doutorado—Porto Alegre, RS, Brazil: Programa de Pós-Grduação em
Ciências Criminais da Escola de Direito da Pontifícia Universidade
Católica do Rio Grande do Sul, 2020.
DAHL, Ö. The
Growth and Maintenance of Linguistic
Complexity. Amsterdam: John Benjamins Publishing
Company, 2004. v. 71
DAI, E.; SUN, Y.; WANG, S. Ginger cannot cure cancer: Battling
fake health news with a comprehensive data repository.
Proceedings of the International AAAI Conference on Web and Social
Media. Anais...Atlanta, USA: 2020.
DALE, R.; MAZUR, P. Handling
Conjunctions in Named Entities. (A. Gelbukh,
Ed.)Computational Linguistics and Intelligent Text Processing.
Anais...Berlin, Heidelberg: Springer Berlin Heidelberg,
2007.
DALIANIS, H. Characteristics of
Patient Records and Clinical Corpora. Em: Clinical Text
Mining: Secondary Use of Electronic Patient Records. Cham:
Springer International Publishing, 2018. p. 21–34.
DARJI, H.; MITROVIĆ, J.; GRANITZER, M. German BERT
Model for Legal Named Entity Recognition. Proceedings of
the 15th International Conference on Agents and Artificial Intelligence.
Anais...SCITEPRESS - Science; Technology Publications,
2023.
DARPA (ED.). Proceedings of the 3rd Message Understanding
Conference (MUC-3). San Diego, EUA: Morgan Kaufmann, 1991.
DE OLIVEIRA, J. M.; ANTUNES, R. S.; DA COSTA, C. A. SOAP classifier for
free-text clinical notes with domain-specific pre-trained language
models. Expert Systems with Applications, v. 245,
p. 123046, 2024.
DE PAIVA, V.; RADEMAKER, A.; MELO, G. DE. OpenWordNet-PT: An
Open Brazilian Wordnet for Reasoning.
Proceedings of COLING 2012: Demonstration Papers.
Anais...2012.
DE SOUSA, S. C.; AZIZ, W.; SPECIA, L. Assessing the post-editing
effort for automatic and semi-automatic translations of DVD
subtitles. Proceedings of the International Conference Recent
Advances in Natural Language Processing 2011.
Anais...2011.
DE SOUZA, M. C. et al. Keywords attention for
fake news detection using few positive labels. Information
Sciences, v. 663, p. 120300, 2024.
DEERWESTER, S. et al. Indexing by latent semantic analysis.
Journal of the American society for information
science, v. 41, n. 6, p. 391–407, 1990.
DEJONG, G. Prediction and substantiation: A new approach to natural
language processing. Cognitive Science, v. 3, n. 3, p.
251–273, 1979.
DEL CORRO, L.; GEMULLA, R. Clausie: clause-based open
information extraction. Proceedings of the 22nd international
conference on World Wide Web. Anais...: WWW ’13.New
York, NY, USA: ACM; ACM, 2013. Disponível em: <http://doi.acm.org/10.1145/2488388.2488420>
DELL’ORLETTA, F.; MONTEMAGNI, S.; VENTURI, G. Read-it: Assessing
readability of italian texts with a view to text simplification.
Proceedings of the 2nd Workshop on Speech and Language
Processing for Assistive Technologies, p. 73–83, 2011.
DEMNER-FUSHMAN, D.; CHAPMAN, W. W.; MCDONALD, C. J. What can natural
language processing do for clinical decision support? J Biomed
Inform, v. 42, n. 5, p. 760–772, ago. 2009.
DESPOTOVIC, V. et al. Detection of
COVID-19 from voice, cough and breathing patterns: Dataset and
preliminary results. Computers in Biology and
Medicine, v. 138, p. 104944, 2021.
DEVARAJU, A. et al. FAIRsFAIR Data Object Assessment
Metrics 0.5. [s.l.] Research Data Alliance (RDA), out.
2020. Disponível em: <https://zenodo.org/record/6461229>.
DEVLIN, J. et al. BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding. (J.
Burstein, C. Doran, T. Solorio, Eds.)Proceedings of the 2019 Conference
of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, NAACL-HLT 2019.
Anais...Minneapolis, MN, USA: Association for
Computational Linguistics, 2019. Disponível em: <https://doi.org/10.18653/v1/n19-1423>
DIAS, M. S. et al. A qualitative analysis of a corpus of opinion
summaries based on aspects. Proceedings of the 1st Workshop on
Tools and Resources for Automatically Processing Portuguese and Spanish.
Anais...2014.
DIAS-DA-SILVA, B. C.; MORALES, H. R. DE. A Construção de um Thesaurus
Eletrônico para o Português. Alfa, 2003.
DIAZ, F.; MITRA, B.; CRASWELL, N. Query Expansion with
Locally-Trained Word Embeddings. Proceedings of the 54th Annual
Meeting of the Association for Computational Linguistics.
Anais...2016.
DODDINGTON, G. Automatic Evaluation of Machine Translation
Quality Using N-Gram Co-Occurrence Statistics. Proceedings of
the Second International Conference on Human Language Technology
Research. Anais...: HLT ’02.San Francisco, CA, USA:
Morgan Kaufmann Publishers Inc., 2002.
DODDINGTON, G. et al. The Automatic Content Extraction
(ACE) Program: Tasks, Data, and Evaluation. (M. T. Lino
et al., Eds.)Proceedings of LREC’2004, Fourth International
Conference on Language resources and Evaluation (Lisboa, 26-28 May
2004). Anais...2004. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2004/pdf/5.pdf>
DOHARE, S.; GUPTA, V.; KARNICK, H. Unsupervised semantic
abstractive summarization. Proceedings of ACL 2018, Student
Research Workshop. Anais...Melbourne, Australia:
Association for Computational Linguistics (ACL), 2018.
DOHERTY, S. et al. Mapping the industry I:
Findings on translation technologies and quality
assessment. QTLaunchPad – Mapping the Industry I: Findings on
Translation Technologies and Quality Assessment.
Anais...GALA, 2013. Disponível em: <http://doras.dcu.ie/19474/1/Version_Participants_Final.pdf>.
Acesso em: 11 nov. 2015
DOHERTY, S. et al. On Education and
Training in Translation Quality Assessment. Em: MOORKENS, J. et al.
(Eds.). Translation Quality Assessment: From Principles to
Practice. Cham: Springer International Publishing, 2018. p.
95–106.
DORR, B. et al. Machine translation evaluation and optimization. Em:
Handbook of Natural Language Processing and Machine Translation:
DARPA Global Autonomous Language Exploitation. [s.l.] Springer,
2011. p. 745–843.
DOSOVITSKIY, A. et al. An Image is Worth 16x16 Words:
Transformers for Image Recognition at Scale. International
Conference on Learning Representations. Anais...2021.
DOZIER, C. et al. Named Entity
Recognition and Resolution in Legal Text. Em: FRANCESCONI, E. et al.
(Eds.). Semantic Processing of Legal Texts: Where the Language
of Law Meets the Law of Language. Berlin, Heidelberg: Springer
Berlin Heidelberg, 2010. p. 27–43.
DUBAY, W. Robert
Gunning’s Fog Readability Formula. Plain Language At Work
Newsletter, v. 8, 2014.
DUBAY, W. H. Smart Language: Readers, Readability, and the
Grading of Text. Costa Mesa, CA: Impact Information, 2007.
DUTRA, L. Evaluating the Contribution of Framenet to
Gender-Based Violence Identification: How semantic annotation can be
used as a resource for identifying patterns of violence.
Dissertação (Mestrado em Tecnologia da Linguagem)—Gotemburgo:
Universidade de Gotemburgo, 2024.
EARL, L. L. Experiments in automatic extracting and indexing.
Information Storage and Retrieval, v. 6, n. 4, p.
313–330, 1970.
EDMUNDSON, H. P. New methods in automatic extracting. Journal of
the ACM (JACM), v. 16, n. 2, p. 264–285, 1969.
EHRET, K. An
Information-Theoretic View on Language Complexity and Register
Variation: Compressing Naturalistic Corpus Data.
Corpus Linguistics and Linguistic Theory, v. 17, n. 2,
p. 383–410, out. 2021.
EHRET, K. et al. Measuring Language
Complexity: Challenges and Opportunities. Linguistics
Vanguard, v. 9, n. s1, p. 1–8, maio 2023.
EHRET, K.; SZMRECSANYI, B. An
Information-Theoretic Approach to Assess Linguistic Complexity. Em:
BAECHLER, R.; SEILER, G. (Eds.). Complexity,
Isolation, and Variation. [s.l.] De
Gruyter, 2016. p. 71–94.
EIGE. European Institute for Gender
Equality. European Institute for Gender
Equality, 2024. Disponível em: <https://eige.europa.eu/gender-based-violence/what-is-gender-based-violence>.
Acesso em: 6 abr. 2024
EISENSTEIN, J. Introduction to Natural Language
Processing. [s.l.] The MIT Press, 2019.
ELLIOT, N.; KLOBUCAR, A. Handbook of automated essay evaluation: Current
applications and new directions. Em: SHERMIS, M. D.; BURSTEIN, J.
(Eds.). [s.l.] Routledge/Taylor & Francis Group, 2013. p. 16–35.
ERDOĞAN, Y. E.; NARIN, A. COVID-19 detection with traditional and deep
features on cough acoustic signals. Computers in Biology and
Medicine, v. 136, p. 104765, 2021.
ERMAKOVA, L.; COSSU, J. V.; MOTHE, J. A survey on evaluation of
summarization methods. Information processing &
management, v. 56, n. 5, p. 1794–1814, 2019.
ESTRELLA, P.; POPESCU-BELIS, A.; KING, M. The
FEMTI guidelines for contextual MT evaluation:
principles and resources. Em: WALTER DAELEMANS; VÉRONIQUE HOSTE
(Eds.). Evaluation of translation
Technology. Linguistica Antverpiensia
new Series- themes em Translation
Studies. [s.l: s.n.].
ETZIONI, O. et al. Unsupervised named-entity extraction from the web: An
experimental study. Artificial intelligence, v. 165, n.
1, p. 91–134, 2005.
FADER, A.; SODERLAND, S.; ETZIONI, O. Identifying Relations for
Open Information Extraction. Proceedings of the 2011 Conference
on Empirical Methods in Natural Language Processing.
Anais...Edinburgh, Scotland, UK.: Association for
Computational Linguistics, jul. 2011. Disponível em: <https://www.aclweb.org/anthology/D11-1142>
FAGHERAZZI, G. et al. Voice for health: the use of vocal biomarkers from
research to clinical practice. Digital biomarkers, v.
5, n. 1, p. 78–88, 2021.
FAIR DATA MATURITY MODEL WORKING GROUP RDA. FAIR Data
Maturity Model. Specification and Guidelines. Research
Data Alliance; Zenodo, 2020. Disponível em: <https://doi.org/10.15497/rda00050>
FARHANGIAN, F.; CRUZ, R. M. O.; CAVALCANTI, G. D. C. Fake news detection:
Taxonomy and comparative study. Information Fusion,
v. 103, p. 102140, 2024.
FARIAS, D. S. et al. Opinion-Meter: A Framework for Aspect-Based
Sentiment Analysis. Proceedings of the 22nd Brazilian Symposium
on Multimedia and the Web. Anais...2016.
FARZINDAR, A.; INKPEN, D. Natural Language Processing for Social
Media. Second edition ed. [s.l.] Morgan; Claypool, 2018.
FASEEH, M. et al. Hybrid approach to automated essay scoring:
Integrating deep learning embeddings with handcrafted linguistic
features for improved accuracy. Mathematics, v. 12, n.
21, p. 3416, 2024.
FAUSTINI, P. H. A.; COVÕES, T. F. Fake news detection in
multiple platforms and languages. Expert Systems with
Applications, v. 158, p. 113503, 2020.
FAUSTINI, P.; COVÕES, T. F. Fake News Detection
Using One-Class Classification. Proceedings of the 8th
Brazilian Conference on Intelligent Systems (BRACIS’19).
Anais...Salvador, BA, Brazil: IEEE, out. 2019.
FEDERICO, M. et al. Assessing the Impact of Translation Errors
on Machine Translation Quality with Mixed-effects Models.
Proceedings of the 2014 Conference on Empirical Methods in Natural
Language Processing (EMNLP). Anais...Doha,
Qatar: Association for Computational Linguistics, out. 2014. Disponível
em: <https://aclanthology.org/D14-1172>
FELLBAUM, C. WordNet: An
Electronic Lexical Database. [s.l.] The MIT Press,
1998.
FELTRIM, V. D. et al. A Construção de uma Ferramenta de Auxílio
à Escrita de Resumos Acadêmicos em Português. Anais do Encontro
Nacional de Inteligência Artificial (ENIA’2003).
Anais...SBC, 2003.
FELTRIN, G. R.; VIANNA, D.; SILVA, A. DA. Um Estudo Sobre
Métricas de Avaliação para Sumarização de Acórdãos. Anais do
XXXVIII Simpósio Brasileiro de Bancos de Dados.
Anais...SBC, 2023.
FENNELLY, O. et al. Use of standardized terminologies in clinical
practice: A scoping review. Int J Med Inform, v. 149,
p. 104431, fev. 2021.
FERNANDES, J. M.; WON, M.; MARTINS, B. Speechmaking and the
Selectorate: Persuasion in Nonpreferential Electoral Systems.
Comparative Political Studies, v. 53, n. 5, p. 667–699,
a2020.
FERNANDES, W. P. D. et al. Appellate court
modifications extraction for Portuguese. Artificial
Intelligence and Law, v. 28, n. 3, p. 327–360, b2020.
FERNANDES-SVARTMAN, F. et al. Temporal prosodic cues for
COVID-19 in Brazilian Portuguese speakers. Proc. Speech
Prosody 2022. Anais...2022.
FERRÁNDEZ, Ó. et al. Tackling HAREM’s portuguese named entity
recognition task with spanish resources. Reconhecimento de
entidades mencionadas em português:
Documentação e actas do HAREM, a primeira
avaliação conjunta na área.
Linguateca (http://www. linguateca.
pt/aval_conjunta/LivroHAREM/Cap11-SantosCardoso2007-Ferrandezetal.
pdf), 2007.
FERREIRA, A. C. et al. Padrões linguísticos para detecção de ironia em
múltiplos idiomas. Revista Gestão & Tecnologia,
2017.
FERREIRA MELLO, R. et al. Towards automated content analysis of
rhetorical structure of written essays using sequential
content-independent features in Portuguese. (A. F. Wise, R.
Martinez-Maldonado, I. Hilliger, Eds.)LAK22 Conference
Proceedings. Anais...United States of America:
Association for Computing Machinery (ACM), 2022.
FERREIRA, R. et al. Towards Automatic Content Analysis of Rhetorical
Structure in Brazilian College Entrance Essays. Em: [s.l:
s.n.]. p. 162–167.
FERREIRA-PAIVA, L. et al. A survey of data augmentation for
audio classification. Congresso Brasileiro de
Automática-CBA. Anais...2022.
FIAD, R. S. Reescrita, dialogismo e etnografia. Linguagem em
(Dis) curso, v. 13, p. 463–480, 2013.
FILLMORE, C. J. Frame semantics. Em: KOREA, T. L. S. OF (Ed.).
Linguistics in the Morning Calm. Seoul: Hanshin, 1982.
FILLMORE, C. J. Frames and the Semantics of Understanding.
Quaderni di Semantica, v. 6, n. 2, p. 222–254, 1985.
FILLMORE, C. J.; JOHNSON, C. R.; PETRUCK, M. R. L. Background to FrameNet.
International Journal of Lexicography, v. 16, n. 3, p.
235–250, 2003.
FINATTO, M. J. B. Projeto PorPopular, frequência de verbos em português
e no jornal popular popular brasileiro. Em: UFMS/LABORATÓRIO DE EDIÇÃO
DA FALE-UFMG, E. DA (Ed.). As Ciências do Léxico: lexicologia,
lexicografia, terminologia. 1. ed. [s.l.] Aparecida Negri
Isquerdo; Maria Cândida Trindade da Costa de Seabra, 2012. v. VIp.
227–244.
FINATTO, M. J. B. Humanidades digitais e
estudos históricos do léxico. Domínios de
Lingu@gem, v. 17, p. e1769, 2023.
FINATTO, M. J. B.; ESTEVES, F. F.; VILLAR, G. S. Construindo
uma terminologia de raiz: textos legislativos sob exploração
terminológica. Revista Platô, v. 5, n. 9, 2022.
FINATTO, M. J. B.; PARAGUASSU, L. B. Acessibilidade textual e
terminológica. 2022.
FINATTO, M. J.; GONÇALVES, M. F.; LAZZARI, R. Léxico e
terminologia em um novo gênero textual do
século XVIII: o manual para enfermeiros. In:
Natalia Terrón Vinagre & Jenny Brumme (orgs.) Emergencia de nuevos
géneros textuales y terminología en la historia de los lenguajes de
especialidad., 2023.
FINE, K. Truthmaker semantics. A Companion to the Philosophy of
Language, p. 556–577, 2017.
FINGER, M. Técnicas de otimização
da precisão empregadas no etiquetador Tycho Brahe.
Proceedings of the International Conference on the Computational
treatment of Portuguese, PROPOR, 2000.
FINGER, M. et al. SPIRA-BM: Biomarkers for Respiratory
Conditions by Audio Analysis via Artificial Intelligence. Anais
do XXV Simpósio Brasileiro de Computação Aplicada à Saúde.
Anais...Porto Alegre, RS, Brasil: SBC, 2025. Disponível
em: <https://sol.sbc.org.br/index.php/sbcas/article/view/35566>
FIRDAUS SOLIHIN, R. F. A., Indra Budi; MAKARIM, E. Advancement of
information extraction use in legal documents. International
Review of Law, Computers & Technology, v. 35, n. 3, p.
322–351, 2021.
FISCHER, M. et al. Identifying Fake News in Brazilian
Portuguese. Natural Language Processing and Information
Systems: 27th International Conference on Applications of Natural
Language to Information Systems, NLDB 2022, Valencia, Spain, June 15–17,
2022, Proceedings. Anais...Berlin, Heidelberg:
Springer-Verlag, 2022. Disponível em: <https://doi.org/10.1007/978-3-031-08473-7_10>
FLORES, F. N.; MOREIRA, V. P.; HEUSER, C. A. Assessing the
impact of stemming accuracy on information retrieval.
International Conference on Computational Processing of the Portuguese
Language. Anais...Springer, 2010.
FLORIAN, R. et al. Named entity recognition through classifier
combination. Proceedings of the seventh conference on Natural
language learning at HLT-NAACL 2003. Anais...2003.
FONSECA, E. R. et al. Automatically Grading Brazilian Student
Essays. (A. Villavicencio et al., Eds.)Computational Processing
of the Portuguese Language. Anais...Springer
International Publishing, 2018.
FONT LLITJÓS, A.; CARBONELL, J. G.; LAVIE, A. A framework for
interactive and automatic refinement of transfer-based machine
translation. Proceedings of the 10th EAMT Conference: Practical
applications of machine translation. Anais...Budapest,
Hungary: European Association for Machine Translation, 2005. Disponível
em: <https://aclanthology.org/2005.eamt-1.13>
FORCADA, M. L.; ÑECO, R. P. Recursive hetero-associative
memories for translation. International Work-Conference on
Artificial Neural Networks. Anais...Springer, 1997.
FORNACIARI, T.; POESIO, M. Automatic deception
detection in Italian court cases. Artif. Intell.
Law, v. 21, n. 3, p. 303–340, set. 2013.
FORTUNA, P. et al. A Hierarchically-Labeled
Portuguese Hate Speech Dataset. Proceedings of the
Third Workshop on Abusive Language Online.
Anais...2019.
FORTUNA, P.; NUNES, S. A survey on automatic detection of hate speech in
text. ACM Computing Surveys (CSUR), 2018.
FOVE. Fove Eye Tracker., 2018. Disponível em: <https://www.getfove.com/>
FREITAS, C. et al. Vampiro que brilha... rá! Desafios na
anotação de opinião em um corpus de resenhas de livros.
Proceedings of XI Encontro de Linguística de Corpus.
Anais...2012.
FREITAS, C. Sobre a construção de um léxico da afetividade para o
processamento computacional do português. Revista Brasileira de
Linguística Aplicada, 2013.
FREITAS, L. A. DE et al. Pathways for irony detection in
tweets. Proceedings of the Symposium on Applied Computing
(SAC). Anais...2014.
FREITAS, L. A. DE. Feature-level sentiment analysis applied to
brazilian portuguese reviews. tese de doutorado—[s.l.]
Pontifícia Universidade Católica do Rio Grande do Sul, 2015.
FREITAS, L. A. DE; SANTOS, L. DOS; DEON, D. Padrões linguísticos para
detecção de ironia em múltiplos idiomas. Revista Eletrônica de
Iniciação Científica em Computação, 2020.
FULLER, C. et al. An Analysis of Text-Based Deception Detection
Tools. Proceedings of the Twelfth Americas Conference on
Information Systems. Anais...2006.
GALHARDI, C. P. et al. Fato ou
Fake? Uma análise da
desinformação frente à pandemia
da COVID-19 no Brasil.
Ciência & Saúde Coletiva,
v. 25, p. 4201–4210, out. 2020.
GAMALLO, P.; GARCIA, M. Multilingual open information
extraction. (F. Pereira et al., Eds.)Portuguese Conference on
Artificial Intelligence. Anais...Cham: Springer;
Springer International Publishing, 2015. Disponível em: <https://doi.org/10.1007/978-3-319-23485-4_72>
GAMALLO, P.; GARCIA, M.; FERNÁNDEZ-LANZA, S. Dependency-based
open information extraction. Proceedings of the joint workshop
on unsupervised and semi-supervised learning in NLP.
Anais...: ROBUS-UNSUP ’12.Stroudsburg, PA, USA:
Association for Computational Linguistics; Association for Computational
Linguistics, 2012. Disponível em: <http://dl.acm.org/citation.cfm?id=2389961.2389963>
GAMBHIR, M.; GUPTA, V. Recent automatic text summarization techniques: a
survey. Artificial Intelligence Review, v. 47, p. 1–66,
2017.
GAMON, M. et al. Handbook of automated essay evaluation: Current
applications and new directions. Em: SHERMIS, M. D.; BURSTEIN, J.
(Eds.). [s.l.] Routledge/Taylor & Francis Group, 2013. p. 251–266.
GAMONAL, M. A. Copa 2014 FrameNet Brasil: Diretrizes para a
Constituição de um Dicionário Eletrônico Trilíngue a partir da Análise
de Frames da Experiência Turística. Dissertação de Mestrado em
Linguística—[s.l.] Universidade Federal de Juiz de Fora, 2013.
GAMONAL, M. A. Modelagem linguístico-computacional de metonímias
na base de conhecimento multilíngue (m.knob) da FrameNet
Brasil. Tese de Doutorado—Juiz de Fora, Brasil: Universidade
Federal de Juiz de Fora, 2017.
GAMONAL, M. A. et al. Audition: A Frame-Annotated Multimodal
Dataset for Accessible Audiovisual Content. 2025.
GAO, M. et al. Human-like Summarization Evaluation with
ChatGPT., 2023. Disponível em: <https://arxiv.org/abs/2304.02554>
GARBIN, C. A. S. et al. Desafios do profissional de saúde na notificação
da violência: obrigatoriedade, efetivação e encaminhamento.
Ciência & Saúde Coletiva,
v. 20, p. 1879–1890, 2015.
GARCIA, E. A. S. et al.
RoBERTaLexPT:
A Legal RoBERTa Model pretrained with
deduplication for Portuguese. (P. Gamallo et al.,
Eds.)Proceedings of the 16th International Conference on Computational
Processing of Portuguese - Vol. 1. Anais...Santiago de
Compostela, Galicia/Spain: Association for Computational Lingustics,
mar. a2024. Disponível em: <https://aclanthology.org/2024.propor-1.38>
GARCIA, G. L. et al. Text Summarization and Temporal Learning
Models Applied to Portuguese Fake News Detection in a Novel
Brazilian Corpus Dataset. (P. Gamallo et al.,
Eds.)Proceedings of the 16th International Conference on Computational
Processing of Portuguese - Vol. 1. Anais...Santiago de
Compostela, Galicia/Spain: Association for Computational Lingustics,
mar. b2024. Disponível em: <https://aclanthology.org/2024.propor-1.9>
GARCIA, G. L.; AFONSO, L. C.; PAPA, J. P. FakeRecogna: a new
brazilian corpus for fake news detection. International
Conference on Computational Processing of the Portuguese Language.
Anais...Springer, 2022.
GARCIA-MOLINA, H. et al. Challenges in data crowdsourcing. IEEE
Transactions on Knowledge and Data Engineering, v. 28, n. 4, p.
901–911, 2016.
GARCIA-MORENO, C.; WATTS, C. Violence against women: an urgent public
health priority. Bulletin of the World Health
Organization, v. 89, p. 2–2, 2011.
GARIJO, D.; POVEDA-VILLALÓN, M. Best Practices for Implementing
FAIR Vocabularies and Ontologies on the Web.
CoRR, v. abs/2003.13084, 2020.
GAUY, M. M. et al. Discriminant Audio Properties In Deep
Learning Based Respiratory Insufficiency Detection In Brazilian
Portuguese. Artificial Intelligence in Medicine: 21st
International Conference on Artificial Intelligence in Medicine, AIME
2023, Portorož, Slovenia, June 12–15, 2023, Proceedings.
Anais...Berlin, Heidelberg: Springer-Verlag, 2023.
Disponível em: <https://doi.org/10.1007/978-3-031-34344-5_32>
GAUY, M. M. et al. Contrasting Deep Learning Models for Direct
Respiratory Insufficiency Detection Versus Blood Oxygen Saturation
Estimation. arXiv preprint arXiv:2407.20989, 2024.
GAUY, M. M.; FINGER, M. Acoustic models of Brazilian Portuguese Speech
based on Neural Transformers. arXiv preprint
arXiv:2312.09265, 2023.
GAUY, M.; FINGER, M. Audio MFCC-gram Transformers for
respiratory insufficiency detection in COVID-19. Anais do XIII
Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana.
Anais...Porto Alegre, RS, Brasil: SBC, 2021. Disponível
em: <https://sol.sbc.org.br/index.php/stil/article/view/17793>
GAZZOLA, M.; LEAL, S. E.; ALUISIO, S. M. Predição da
Complexidade Textual de Recursos Educacionais Abertos em
Português. Proceedings of the Brazilian Symposium in
Information and Human Language Technology.
Anais...2019.
GEMMEKE, J. F. et al. Audio set: An ontology and human-labeled
dataset for audio events. 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP).
Anais...IEEE, 2017.
GEORGE, J. F.; KEANE, B. T. Deception Detection by Disinterested
Third-Party Observers. Proceedings of the Credibility
Assessment and Information Quality in Government and Business Symposium,
39th Hawaii International Conference on System Sciences (HICSS).
Anais...Kauai, HI: 2006.
GEURGAS, R.; TESSLER, L. R. Automatic detection of
fake tweets about the COVID-19 Vaccine in Portuguese. Social
Network Analysis and Mining, v. 14, n. 1, p. 55, 8 mar. 2024.
GHANEM, B. et al. IDAT at FIRE2019: Overview of the Track on
Irony Detection in Arabic Tweets. Proceedings of the 11th Forum
for Information Retrieval Evaluation. Anais...2019.
GHOSH, A. et al. SemEval-2015 Task 11:
Sentiment Analysis of Figurative Language in
Twitter. Proceedings of the 9th International
Workshop on Semantic Evaluation (SemEval
2015). Anais...2015.
GIBBS, R. W.; COLSTON, H. L. The Risks and Rewards of Ironic
Communication. Say not to say: new perspectives on
miscommunication. Anais...2001. Disponível em: <https://api.semanticscholar.org/CorpusID:12510370>
GLAUBER, R. et al. Challenges of an Annotation Task for Open
Information Extraction in Portuguese. (A. Villavicencio et al.,
Eds.)Computational Processing of the Portuguese Language.
Anais...Cham: Springer International Publishing, 2018.
GLAUBER, R.; CLARO, D. B. A systematic mapping
study on open information extraction. Expert Systems with
Applications, v. 112, p. 372–387, 2018.
GLAUBER, R.; CLARO, D. B.; OLIVEIRA, L. S. Dependency Parser on
Open Information Extraction for Portuguese Texts - DptOIE and
DependentIE on IberLEF. Proceedings of the Iberian Languages
Evaluation Forum (IberLEF 2019) co-located with 35th Conference of the
Spanish Society for Natural Language Processing (SEPLN 2019).
Anais...http://ceur-ws.org/Vol-2421/: CEUR Workshop
Proceedings, a2019.
GLAUBER, R.; CLARO, D. B.; SENA, C. F. DE L. Towards a Pragmatic
Open Information Extraction for Portuguese Text - ICEIS17, InferPortOIE
and PragmaticOIE on IberLEF. Proceedings of the Iberian
Languages Evaluation Forum (IberLEF 2019) co-located with 35th
Conference of the Spanish Society for Natural Language Processing (SEPLN
2019). Anais...http://ceur-ws.org/Vol-2421/: CEUR
Workshop Proceedings, b2019.
GÔLO, M. P. S. et al. One-class learning
for fake news detection through multimodal variational autoencoders.
Engineering Applications of Artificial Intelligence, v.
122, p. 106088, 2023.
GÔLO, M. P. S. et al. On the use of Large Language Models to
Detect Brazilian Politics Fake News. Proceedings of the 21st
National Meeting on Artificial and Computational Intelligence
(ENIAC’2024). Anais...Belém, PA, Brazil: Brazilian
Computer Society, nov. 2024.
GOMES, J. R. S. et al. Deep Learning Brasil at ABSAPT 2022:
Portuguese Transformer Ensemble Approaches. Proceedings of the
Iberian Languages Evaluation Forum (IberLEF 2022) co-located with the
Conference of the Spanish Society for Natural Language Processing
(SEPLN 2022), A Coruña, Spain,
September 20, 2022. Anais...2022.
GONÇALO OLIVEIRA, H. et al. Avaliação
à medida no Segundo HAREM. (C. Mota, D. Santos,
Eds.)Desafios na avaliação conjunta do
reconhecimento de entidades mencionadas: O Segundo HAREM.
Anais...Linguateca, 2008.
GONÇALVES, T. et al. Clinical Screening Prediction in the Portuguese
National Health Service: Data Analysis, Machine Learning Models,
Explainability and Meta-Evaluation. Future Internet, v.
15, n. 1, p. 26, 2023.
GORDEEFF, E. M. Avaliação sobre
Animação e Cinema de vida real: semelhanças e diferenças.
Diálogo com a Economia Criativa, v. 8, n. 24, p. 50–63,
2023.
GOURISARIA, M. K. et al. Comparative analysis of audio classification
with MFCC and STFT features using machine learning techniques.
Discover Internet of Things, v. 4, n. 1, p. 1, 2024.
GRAESSER, A. C. et al. Coh-Metrix: Analysis of text on cohesion and
language. Behavior Research Methods, Instruments, n Computer -
Springer, p. 193–202, 2004.
GRAESSER, A. C.; MCNAMARA, D. S.; KULIKOWICH, J. M. Coh-Metrix:
Providing Multilevel Analyses of Text Characteristics.
Educational Researcher Vol. 40, N. 5, p. 223–234, 2011.
GRAHAM, Y. et al. Is all that Glitters in Machine Translation
Quality Estimation really Gold? Proceedings of
COLING 2016: Technical Papers.
Anais...Osaka, Japan: The COLING 2016 Organizing
Committee, dez. 2016. Disponível em: <https://www.aclweb.org/anthology/C16-1294>
GRATTAFIORI, A. et al. The Llama 3 Herd of Models.,
2024. Disponível em: <https://arxiv.org/abs/2407.21783>
GRAVES, A.; MOHAMED, A.; HINTON, G. Speech recognition
with deep recurrent neural networks. 2013 IEEE
International Conference on Acoustics, Speech and Signal Processing.
Anais...2013.
GREENBERG, J. H. Some universals of grammar with particular reference to
the order of meaningful elements. Em: GREENBERG, J. H. (Ed.).
Universals of Grammar. 2. ed. Cambridge, Mass.: MIT
Press, 1966. p. 73–113.
GRICE, H. P. Logic and Conversation. Em: Syntax and Semantics:
Vol. 3: Speech Acts. [s.l.] Academic Press, 1975.
GRISHMAN, R.; SUNDHEIM, B. Message
Understanding Conference- 6: A Brief
History. COLING 1996 Volume 1: The 16th
International Conference on Computational Linguistics.
Anais...1996. Disponível em: <https://aclanthology.org/C96-1079>
GRUBER, A.; WEISS, Y.; ROSEN-ZVI, M. Hidden Topic Markov
Models. Proceedings of the Eleventh International Conference on
Artificial Intelligence and Statistics. Anais...:
Proceedings of Machine Learning Research.San Juan, Puerto Rico: PMLR,
mar. 2007.
GRUPPI, M.; HORNE, B. D.; ADALI, S. NELA-GT-2019:
A Large Multi-Labelled News Dataset for The Study of
Misinformation in News Articles. CoRR, v.
abs/2003.08444, p. 1–5, 2020.
GRUPPI, M.; HORNE, B. D.; ADALI, S. NELA-GT-2020:
A Large Multi-Labelled News Dataset for The Study of
Misinformation in News Articles. CoRR, v.
abs/2102.04567, p. 1–6, 2021.
GU, J. et al. A Survey on LLM-as-a-Judge., 2025.
Disponível em: <https://arxiv.org/abs/2411.15594>
GUARINO, N.; GUIZZARDI, G. We need to Discuss the
Relationship: Revisiting Relationships as Modeling
Constructs. Proceedings of the 27th International
Conference on Advanced Information Systems Engineering (CAISE
2015). Anais...Springer-Verlag,
2015.
GUIMARÃES, G. M. C. et al. Legal Document
Segmentation and Labeling Through Named Entity Recognition
Approaches. Journal of Information and Data
Management, v. 15, n. 1, 2024.
GUIMARÃES, J. A. C.; SANTOS, J. C. G. A ementa
jurisprudencial como resumo informativo em um domı́nio
especializado: aspectos estruturais. Brazilian Journal of
Information Science: research trends, v. 10, n. 3, 2016.
GUIMARÃES, S. S. et al. Characterizing Toxicity on Facebook
Comments in Brazil. Proceedings of the Brazilian Symposium on
Multimedia and the Web. Anais...2020.
GUIZZARDI, G. Ontology, Ontologies and the “I” of
FAIR. Data Int., v. 2, n. 1-2, p. 181–191, 2020.
GULDEN, C. et al. Extractive
summarization of clinical trial descriptions. International
Journal of Medical Informatics, v. 129, p. 114–121, 2019.
GUMIEL, Y. B. et al. Temporal
Relation Extraction in Clinical Texts: A Systematic Review. v. 54,
n. 7, set. 2021.
GUPTA, S.; GUPTA, S. Abstractive summarization: An overview of the state
of the art. Expert Systems with Applications, v. 121,
p. 49–65, 2019.
HABIBI, M. et al. Deep learning with word embeddings improves
biomedical named entity recognition.
Bioinformatics, v. 33, n. 14, p. i37–i48, 2017.
HAENDCHEN FILHO, A. et al. An approach to evaluate adherence to
the theme and the argumentative structure of essays.
International Conference on Knowledge-Based Intelligent Information
& Engineering Systems. Anais...2018.
HAENDCHEN FILHO, A. et al. Imbalanced Learning Techniques for Improving
the Performance of Statistical Models in Automated Essay Scoring.
Procedia Computer Science, v. 159, p. 764–773, jan.
2019.
HAILU, T. T.; YU, J.; FANTAYE, T. G. A framework for word embedding
based automatic text summarization and evaluation.
Information, v. 11, n. 2, p. 78, 2020.
HAKUTA, K. Handbook of Automated Essay Evaluation: Current Applications
and New Directions. Em: SHERMIS, M. D.; BURSTEIN, J. (Eds.). [s.l.]
Routledge/Taylor & Francis Group, 2013. p. 347–353.
HAN, J. et al. Exploring automatic COVID-19 diagnosis via voice
and symptoms from crowdsourced data. ICASSP 2021-2021 IEEE
International Conference on Acoustics, Speech and Signal Processing
(ICASSP). Anais...IEEE, 2021.
HANLEY, J. A.; MCNEIL, B. J. The meaning and use of the area under a
receiver operating characteristic (ROC) curve.
Radiology, v. 143, n. 1, p. 29–36, 1982.
HARTMANN, N. S. et al. Portuguese word embeddings: Evaluating on
word analogies and natural language tasks. Proceedings of
Symposium in Information and Human Language Technology.
Anais...[S.l.: s.n.]: 2017.
HARTMANN, N. S.; ALUÍSIO, S. M. Adaptação Lexical Automática
em Textos Informativos do Português Brasileiro para o Ensino
Fundamental. Linguamática, v. 12, n. 2, p. 3–27,
dez. 2020.
HARTMANN PEIXOTO, F. Projeto
Victor: relato do desenvolvimento da Inteligência Artificial na
Repercussão Geral do Supremo Tribunal Federal. Revista
Brasileira de Inteligência Artificial e Direito - RBIAD, v. 1,
n. 1, p. 1–22, 2020.
HASEGAWA, T.; SEKINE, S.; GRISHMAN, R. Discovering relations
among named entities from large corpora. Proceedings of the
42nd Annual Meeting of the Association for Computational Linguistics
(acl-04). Anais...2004.
HASSAN, A.; SHAHIN, I.; ALSABEK, M. B. COVID-19 detection system
using recurrent neural networks. 2020 International conference
on communications, computing, cybersecurity, and informatics (CCCI).
Anais...IEEE, 2020.
HASSAN, H. et al. Achieving Human Parity on Automatic
Chinese to English News Translation.
arXiv preprint 1803.05567, 2018.
HAUCH, V. et al. Linguistic Cues to Deception Assessed by
Computer Programs: A Meta-analysis. Proceedings of the Workshop
on Computational Approaches to Deception Detection.
Anais...2012.
HE, K. et al. Masked autoencoders are scalable vision
learners. Proceedings of the IEEE/CVF conference on computer
vision and pattern recognition. Anais...2022.
HEARST, M. A. Automatic acquisition of hyponyms from large text
corpora. Proceedings of the 14th conference on Computational
linguistics-Volume 2. Anais...Association for
Computational Linguistics, 1992.
HEE, C. V.; LEFEVER, E.; HOSTE, V.
SemEval-2018 Task 3: Irony Detection
in English Tweets. Proceedings of the 12th
International Workshop on Semantic Evaluation.
Anais...2018.
HEINRICH, T.; MARCHI, F. TeamUFPR at ABSAPT 2022:
Aspect Extraction with CRF and BERT.
Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)
co-located with the Conference of the Spanish Society for Natural
Language Processing (SEPLN 2022), A
Coruña, Spain, September 20, 2022.
Anais...2022.
HEUSDEN, R. VAN; KAMPS, J.; MARX, M. Neural Coreference Resolution
for Dutch Parliamentary Documents with the DutchParliament Dataset.
Data, v. 8, n. 2, 2023.
HITZLER, A. H. Y. A. M. S. M. A. G. B. A. W. R. A. A. S. A. A. H. M. A.
K. T. A. J. M. M. A. A. M. A. J. P. A. P. Multimodal mental
health analysis in social media. PLOS ONE, v. 15,
n. 4, p. 1–27, 2020.
HOCHREITER, S.; SCHMIDHUBER, J. Long Short-Term
Memory. Neural Computation, v. 9, n. 8, p.
1735–1780, nov. 1997.
HOCKETT, C. F. A Course in Modern Linguistics. [s.l.]
Macmillan, 1958.
HOFMANN, T. Probabilistic Latent Semantic Indexing.
Proceedings of the 22nd Annual International ACM
SIGIR Conference on Research and Development in Information
Retrieval (SIGIR ’99). Anais...New York,
NY, USA: Association for Computing Machinery, 1999.
HOVY, E.; KING, M.; POPESCU-BELIS, A. An introduction to MT
evaluation. Proceedings of Machine Translation Evaluation:
Human Evaluators meet Automated Metrics. Workshop at the LREC 2002
Conference. Las Palmas, Spain. Anais...2002.
HSU, W.-N. et al. Hubert: Self-supervised speech representation learning
by masked prediction of hidden units. IEEE/ACM Transactions on
Audio, Speech, and Language Processing, v. 29, p. 3451–3460,
2021.
HU, M.; LIU, B. Mining Opinion Features in Customer
Reviews. Proceedings of the 19th National Conference on
Artifical Intelligence. Anais...2004.
HUA, Y.; DENG, Z.; MCKEOWN, K. Improving Long Dialogue
Summarization with Semantic Graph Representation. (A. Rogers,
J. Boyd-Graber, N. Okazaki, Eds.)Findings of the Association for
Computational Linguistics: ACL 2023. Anais...Toronto,
Canada: Association for Computational Linguistics, jul. 2023. Disponível
em: <https://aclanthology.org/2023.findings-acl.871/>
HUANG, P.-Y. et al. Masked autoencoders that listen. Advances in
Neural Information Processing Systems, v. 35, p. 28708–28720,
2022.
HUSSAIN, A. S.; THOMAS, A. Large Language Models for Judicial
Entity Extraction: A Comparative Study., 2024. Disponível em:
<https://arxiv.org/abs/2407.05786>
HUTCHINS, J. Towards a definition of example-based machine
translation., Proceedings of Second
Workshop on Example-Based
Machine Translation;
Anais...2005.
HUTCHINS, W. Machine Translation: A Concise History. Journal of
Translation Studies: Special Issue on The Teaching of Computer-aided
Translation, v. 13, p. 1–2, 2010.
HUTCHINS, W. J. Machine
translation over fifty years. Histoire, Epistemologie,
Langage, v. XXII, n. 1, p. 7–31, 2001.
IFTIKHAR, A.; UL QOUNAIN JAFFRY, S. W.; MALIK, M. K. Information Mining
From Criminal Judgments of Lahore High Court. IEEE
Access, v. 7, p. 59539–59547, 2019.
IMOTIONS. Eye Tracking - The Complete Pocket Guide.
[s.l.] www.imotions.com, 2017.
IOFFE, S.; SZEGEDY, C. Batch normalization: Accelerating deep
network training by reducing internal covariate shift.
International conference on machine learning.
Anais...PMLR, 2015.
IPM. INAF
Brasil 2018: Indicador de Alfabetismo Funcional -
Resultados Preliminares. Instituto Paulo
Montenegro, 2018.
JACOBS, R. A. et al. Adaptive mixtures of local experts. Neural
computation, v. 3, n. 1, p. 79–87, 1991.
JACOBSEN, A. et al. FAIR principles: interpretations and
implementation considerations. Data
intelligenceMIT Press One Rogers Street, Cambridge, MA
02142-1209, USA journals-info …, 2020.
JAHAN, M. S.; OUSSALAH, M. A systematic review of hate speech automatic
detection using natural language processing.
Neurocomputing, 2023.
JARGAS, A. M. Expressões
Regulares - 5a edição: Uma Abordagem
Divertida. [s.l.] Novatec Editora, 2016.
JÄRVELIN, K.; KEKÄLÄINEN, J. Cumulated gain-based evaluation of IR
techniques. ACM Transactions on Information Systems
(TOIS), v. 20, n. 4, p. 422–446, 2002.
JERONIMO, C. et al. Characterization of Fake News
Based on Subjectivity Lexicons. Journal of Data
Intelligence, v. 1, p. 419–441, dez. 2020.
JIANG, A. Q. et al. Mistral 7B., 2023. Disponível em:
<https://arxiv.org/abs/2310.06825>
JIANG, S. et al. Multi-Ontology Refined
Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic
representation model for biomedical concepts. Journal of
Biomedical Informatics, v. 111, p. 103581, 2020.
JIANG, S. et al. Irony Detection in the Portuguese Language
using BERT. Proceedings of the Iberian Languages
Evaluation Forum (IberLEF 2021) co-located with the Conference of the
Spanish Society for Natural Language Processing (SEPLN
2021), XXXVII International Conference of the Spanish
Society for Natural Language Processing., Málaga, Spain,
September, 2021. Anais...2021.
JOHNSTONE, M. A.-M. A. T. In an Absolute State:
Elevated Use of Absolutist Words Is a Marker Specific to Anxiety,
Depression, and Suicidal Ideation. Clinical Psychological
Science, v. 6, n. 4, p. 529–542, 2018.
JONES, K. H. et al. Toward the Development of Data Governance Standards
for Using Clinical Free-Text Data in Health Research:
Position Paper. J Med Internet Res, v. 22, n. 6, p.
e16760, jun. 2020.
JONES, K. S. What might be in a summary? Information
retrieval, v. 93, n. 1, p. 9–26, 1993.
JONNALAGADDA, S.; GONZALEZ, G. Biosimplify: an open source sentence
simplification engine to improve recall in automatic biomedical
information extraction. AMIA Annual Symposium
Proceedings, p. 351–356, 2010.
JULIÃO, A. Algoritmo do Google: Veja o impacto que tem no
SEO. Disponível em: <https://blog.ajestrategia.com.br/algoritmo-do-google-veja-o-impacto-que-tem-no-seo/>.
JUOLA, P. Measuring
Linguistic Complexity: The Morphological Tier.
Journal of Quantitative Linguistics, v. 5, n. 3, p.
206–213, dez. 1998.
JUOLA, P. Assessing
Linguistic Complexity. Em: MIESTAMO, M.; SINNEMÄKI, K.; KARLSSON, F.
(Eds.). Studies in Language Companion
Series. Amsterdam: John Benjamins Publishing Company,
2008. v. 94p. 89–108.
JURAFSKY, D.; MARTIN, J. H. Speech and Language Processing: An
Introduction to Natural Language Processing, Computational Linguistics,
and Speech Recognition. 3rd. ed. USA: Prentice Hall PTR, 2023.
JUSTIÇA - CNJ, C. N. DE. Conselho Nacional de Justiça — Justiça
em Números. https://www.cnj.jus.br/pesquisas-judiciarias/justica-em-numeros/,
maio 24DC.
KAHLE, P. et al. Transkribus-a service platform for
transcription, recognition and retrieval of historical
documents. 2017 14th IAPR International Conference on Document
Analysis and Recognition (ICDAR). Anais...IEEE, 2017.
KAMBHATLA, N. Combining lexical, syntactic, and semantic
features with maximum entropy models for information
extraction. Proceedings of the ACL interactive poster and
demonstration sessions. Anais...2004.
KAPETANIDIS, P. et al. Respiratory diseases diagnosis using audio
analysis and artificial intelligence: a systematic review.
Sensors, v. 24, n. 4, p. 1173, 2024.
KAPOOR, A. et al. HLDC:
Hindi Legal Documents Corpus. Findings of the
Association for Computational Linguistics: ACL 2022.
Anais...Association for Computational Linguistics,
2022.
KARELITZ, J. L.; MICHAEL, V. C.; PERKINS, K. A. Analysis of agreement between
expired-air carbon monoxide monitors. Journal of smoking
cessation, v. 2, n. 12, p. 105–112, 2017.
KATCHAPAKIRIN, K. et al. Facebook Social Media
for Depression Detection in the Thai Community. 15th
International Joint Conference on Computer Science and Software
Engineering (JCSSE). Anais...2018.
KHAYRALLAH, H.; KOEHN, P. On the Impact of Various Types of
Noise on Neural Machine Translation. Proceedings of the 2nd
Workshop on Neural Machine Translation and Generation.
Anais...Melbourne, Australia: Association for
Computational Linguistics, jul. 2018. Disponível em: <https://aclanthology.org/W18-2709>
KILGARRIFF, A.; AL., ET. PtTenTen: A Corpus for Portuguese Lexicography.
Em: Working with Portuguese Corpora. [s.l: s.n.]. p.
111–130.
KINCAID, J. P. et al. Derivation of new readability formulas (automated
readability index, fog count, and flesch reading ease formula) for Navy
enlisted personnel. Research Branch Report, p. 8–75,
1975.
KIND, L. et al. Subnotificação e (in) visibilidade da violência contra
mulheres na atenção primária à saúde. Cadernos de
Saúde Pública, v. 29, p. 1805–1815,
2013.
KOEHN, P. et al. Moses: Open Source Toolkit for
Statistical Machine Translation. Proceedings of the 45th Annual
Meeting of the Association for Computational Linguistics Companion
Volume Proceedings of the Demo and Poster Sessions.
Anais...Prague, Czech Republic: Association for
Computational Linguistics, jun. 2007. Disponível em: <https://aclanthology.org/P07-2045>
KOEHN, P. Statistical Machine
Translation. [s.l.] Cambridge University Press, 2009.
KOEHN, P. Neural
Machine Translation. [s.l.] Cambridge University Press,
2020.
KOEHN, P.; OCH, F. J.; MARCU, D. Statistical phrase-based
translation. Proceedings of the 2003 Conference of the North
American Chapter of the Association for Computational Linguistics on
Human Language Technology - NAACL ’03.
Anais...Association for Computational Linguistics,
2003. Disponível em: <http://dx.doi.org/10.3115/1073445.1073462>
KOHAVI, R. A Study of Cross-Validation and Bootstrap for
Accuracy Estimation and Model Selection. Proceedings of the
14th International Joint Conference on Artificial Intelligence (IJCAI).
Anais...1995.
KOHO, M. et al. WarSampo
Knowledge Graph: Finland in the Second World War as Linked Open
Data. Semantic Web – Interoperability, Usability,
Applicability, v. 12, n. 2, p. 265–278, 2021.
KOLECK, T. A. et al. Natural language processing of symptoms documented
in free-text narratives of electronic health records: a systematic
review. J Am Med Inform Assoc, v. 26, n. 4, p. 364–379,
abr. 2019.
KONG, Q. et al. Cross-Task Learning for Audio Tagging, Sound
Event Detection and Spatial Localization: DCASE 2019
Baseline Systems. [s.l.] DCASE2019 Challenge, 2019.
KONG, Q. et al. Panns: Large-scale pretrained audio neural networks for
audio pattern recognition. IEEE/ACM Transactions on Audio,
Speech, and Language Processing, v. 28, p. 2880–2894, 2020.
KONSTANTINOVA, N. Review of relation extraction methods: What is
new out there? Analysis of Images, Social Networks and Texts:
Third International Conference, AIST 2014, Yekaterinburg, Russia, April
10-12, 2014, Revised Selected Papers 3.
Anais...Springer, 2014.
KORNILOVA, A.; EIDELMAN, V. BillSum:
A Corpus for Automatic Summarization of US
Legislation. Proceedings of the 2nd Workshop on New
Frontiers in Summarization. Anais...Association for
Computational Linguistics, 2019.
KRINGS, H. P. Repairing Texts:
Empirical Investigations of Machine Translation Post-editing
Processes. [s.l.] Kent State University Press, 2001.
KULKARNI, M. et al. Towards a Unified Multi-Domain Multilingual
Named Entity Recognition Model. Proceedings of the 17th
Conference of the European Chapter of the Association for Computational
Linguistics. Anais...Dubrovnik, Croatia: Association
for Computational Linguistics, 2023. Disponível em: <https://aclanthology.org/2023.eacl-main.161>
KUMAR, D. et al. Understanding the Behaviors of Toxic Accounts
on Reddit. Proceedings of the ACM Web Conference 2023.
Anais...2023.
KUZI, S.; SHTOK, A.; KURLAND, O. Query expansion using word
embeddings. Proceedings of the 25th ACM international on
conference on information and knowledge management.
Anais...2016.
LAFFERTY, J. D.; MCCALLUM, A.; PEREIRA, F. C. N. Conditional
Random Fields: Probabilistic Models for Segmenting and Labeling Sequence
Data. Proceedings of the Eighteenth International Conference on
Machine Learning. Anais...: ICML ’01.San Francisco, CA,
USA: Morgan Kaufmann Publishers Inc., 2001. Disponível em: <https://dl.acm.org/doi/abs/10.5555/645530.655813>
LAGUARTA, J.; HUETO, F.; SUBIRANA, B. COVID-19 artificial
intelligence diagnosis using only cough recordings. IEEE
Open Journal of Engineering in Medicine and Biology, v. 1, p.
275–281, 2020.
LAMPLE, G. et al. Neural Architectures for
Named Entity Recognition. (K. Knight, A. Nenkova, O.
Rambow, Eds.)Proceedings of the 2016 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies. Anais...San
Diego, California: Association for Computational Linguistics, jun. 2016.
LANDRY, V. et al. Audio-based digital biomarkers in diagnosing and
managing respiratory diseases: a systematic review and bibliometric
analysis. European Respiratory Review, v. 34, n. 176,
2025.
LÄUBLI, S. et al. A set of recommendations for assessing human–machine
parity in language translation. Journal of Artificial
Intelligence Research, v. 67, p. 653–672, 2020.
LÄUBLI, S.; SENNRICH, R.; VOLK, M. Has Machine Translation
Achieved Human Parity? A Case for Document-level
Evaluation. Proceedings of EMNLP.
Anais...Brussels, Belgium: 2018.
LAZER, D. M. J. et al. The science of fake news.
Science, v. 359, n. 6380, p. 1094–1096, 2018.
LAZZARI, R. R.; FINATTO, M. J. B. Exame do vocabulário
médico no Português no século
XVIII: contribuições da lexicometria para o
desenho de um dicionário histórico.
Mandinga-Revista de Estudos Linguı́sticos (ISSN:
2526-3455), v. 7, n. 1, p. 102–123, 2023.
LEACOCK, C. et al. Automated Grammatical Error Detection for
Language Learners. [s.l.] Morgan; Claypool Publishers, 2010.
LEAL, S. E. et al. Avaliação automática da complexidade de
sentenças do português brasileiro para o domínio rural.
Symposium in Information and Human Language Technology - STIL.
Anais...SBC, 2019.
LEAL, S. E. et al. Using Eye-tracking Data to Predict the
Readability of Brazilian Portuguese Sentences
in Single-task, Multi-task and Sequential Transfer Learning
Approaches. Proceedings of the 28th International Conference on
Computational Linguistics. Anais...Barcelona, Spain
(Online): International Committee on Computational Linguistics, dez.
2020. Disponível em: <https://www.aclweb.org/anthology/2020.coling-main.512>
LEAL, S. E. Predição da complexidade sentencial do português
brasileiro escrito, usando métricas linguísticas, psicolinguísticas e de
rastreamento ocular. tese de doutorado—[s.l.] Universidade de
São Paulo, 2021.
LEAL, S. E. et al. RastrOS Project: Natural Language Processing
contributions to the development of an eye-tracking corpus with
predictability norms for Brazilian Portuguese. Language
Resources and Evaluation, p. 1333–1372, 2022.
LEAL, S. E. et al. NILC-Metrix: assessing
the complexity of written and spoken language in Brazilian
Portuguese. Language Resources and Evaluation,
2023.
LEAL, S. E.; DURAN, M. S.; ALUÍSIO, S. M. A Nontrivial Sentence
Corpus for the Task of Sentence Readability Assessment in
Portuguese. Proceedings of the 27th International
Conference on Computational Linguistics.
Anais...Association for Computational Linguistics, ago.
2018.
LÉCHELLE, W.; GOTTI, F.; LANGLAIS, P. WiRe57: A Fine-Grained Benchmark
for Open Information Extraction. arXiv preprint
arXiv:1809.08962, 2018.
LEE, J. et al. BioBERT: a
pre-trained biomedical language representation model for biomedical text
mining. Bioinformatics, v. 36, n. 4, p.
1234–1240, set. 2019.
LEE, S. et al. A Survey
on Evaluation Metrics for Machine Translation.
Mathematics, v. 11, n. 4, 2023.
LEHNERT, W.; SUNDHEIM, B. A performance evaluation of text-analysis
technologies. AI magazine, v. 12, n. 3, p. 81–81, 1991.
LEITÃO, M. M.; RIBEIRO, A. J. C.; MAIA, M. Penalidade do Nome Repetido e
Rastreamento Ocular em Português Brasileiro. Revista
LinguíStica, v. v8 n2, 2012.
LEITE, H. et al. WRITEME: uma Ferramenta de Auxílio
à Escrita de READMEs Baseada em Dados Abertos.
Anais do XVII Congresso Latino-Americano de Software Livre e Tecnologias
Abertas. Anais...Porto Alegre, RS, Brasil: SBC, 2020.
LEITNER, E.; REHM, G.; MORENO-SCHNEIDER, J. Fine-Grained Named
Entity Recognition in Legal Documents. (M. Acosta et al.,
Eds.)Semantic Systems. The Power of AI and Knowledge Graphs.
Anais...Cham: Springer International Publishing, 2019.
LESK, M. The seven ages of information retrieval.,
1995. Disponível em: <https://archive.ifla.org/VI/5/op/udtop5/udt-op5.pdf>
LEWIS, M. et al. BART: Denoising
Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension. (D. Jurafsky et al.,
Eds.)Proceedings of the 58th Annual Meeting of the Association for
Computational Linguistics, ACL 2020, Online, July 5-10,
2020. Anais...Association for Computational
Linguistics, 2020. Disponível em: <https://doi.org/10.18653/v1/2020.acl-main.703>
LI, Q.; JI, H. Incremental joint extraction of entity mentions
and relations. Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics (Volume 1: Long Papers).
Anais...2014.
LI, Y. et al. MAGE:
Machine-generated Text Detection in the Wild. (L.-W. Ku, A.
Martins, V. Srikumar, Eds.)Proceedings of the 62nd Annual Meeting of the
Association for Computational Linguistics (Volume 1: Long Papers).
Anais...Bangkok, Thailand: Association for
Computational Linguistics, ago. 2024.
LIKERT, R. A Technique for the
Measurement of Attitudes. [s.l.] Archives of Psychology,
1932.
LIMA, A. DA S.; BORGES, V. R. Training and
evaluating Named Entity Recognition Models using a Legal Corpus of
publications from Government Gazettes. 2022.
LIMA, J. P.; COSTA, J. A.; ARAÚJO, D. C. Comparison of Feature
Extraction Methods for Brazilian Legal Documents Clustering.
2021 IEEE Latin American Conference on Computational Intelligence
(LA-CCI). Anais...IEEE, 2021. Disponível em: <https://doi.org/10.1109/LA-CCI48322.2021.9769839>
LIMA, T. B. DE et al. Avaliação Automática de
Redação: Uma revisáo
sistemática. Revista Brasileira de
Informática na Educação,
v. 31, p. 205--221, maio 2023.
LIN, C. et al. SenseMood: Depression Detection on Social
Media. Em: 2020 International Conference on Multimedia
Retrieval. New York, USA: Association for
Computing Machinery, 2020a. p. 407–411.
LIN, C.-Y. ROUGE: A Package for Automatic
Evaluation of Summaries. Text Summarization Branches Out.
Anais...Barcelona, Spain: Association for Computational
Linguistics, jul. 2004. Disponível em: <https://aclanthology.org/W04-1013>
LIN, H.; NG, V. Abstractive
Summarization: A Survey of the State of the Art. Proceedings
of the AAAI Conference on Artificial Intelligence, v. 33, n.
01, p. 9815–9822, 2019.
LIN, J.; NOGUEIRA, R.; YATES, A. Pretrained Transformers for Text
Ranking: BERT and Beyond. arXiv preprint
arXiv:2010.06467, b2020.
LIU, A. T. et al. Mockingjay: Unsupervised speech representation
learning with deep bidirectional transformer encoders. ICASSP
2020-2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). Anais...IEEE, a2020.
LIU, A. T.; LI, S.-W.; LEE, H. Tera: Self-supervised learning of
transformer encoder representation for speech. arXiv preprint
arXiv:2007.06028, b2020.
LIU, B. Sentiment Analysis and Opinion Mining. Synthesis
Lectures on Human Language Technologies, 2012.
LIU, T.; YAO, J.-G.; LIN, C.-Y. Towards improving neural named
entity recognition with gazetteers. Proceedings of the 57th
annual meeting of the association for computational linguistics.
Anais...a2019.
LIU, X. et al. Evaluating the Factuality of Large Language
Models using Large-Scale Knowledge Graphs., 2024. Disponível
em: <https://arxiv.org/abs/2404.00942>
LIU, Y. et al. RoBERTa: A Robustly Optimized BERT Pretraining
Approach., b2019. Disponível em: <https://arxiv.org/abs/1907.11692>
LIU, Y.; HEARNE, J.; CONRAD, B. Recognizing proper names in ur
iii texts through supervised learning. The Twenty-Ninth
International Flairs Conference. Anais...2016.
LIU, Y.; LAPATA, M. Text summarization with pretrained encoders.
arXiv preprint arXiv:1908.08345, 2019.
LIU, Z. et al. De-identification of clinical notes via recurrent neural
network and conditional random field. J Biomed Inform,
v. 75S, p. S34–S42, jun. 2017.
LIU, Z. et al. A Robustly Optimized BERT Pre-Training Approach
with Post-Training. Chinese Computational Linguistics: 20th
China National Conference, CCL 2021, Hohhot, China, August 13–15, 2021,
Proceedings. Anais...Berlin, Heidelberg:
Springer-Verlag, 2021. Disponível em: <https://doi.org/10.1007/978-3-030-84186-7_31>
LIVESO. O que é BERT? - O mais recente algoritmo da
Google. Disponível em: <https://liveseo.com.br/seo/o-que-e-bert-o-mais-recente-algoritmo-da-google/#:~:text=Bem%2C%20o%20BERT%2C%20de%20maneira,respostas%20possíveis%20para%20seus%20usuários>.
LO, C. YiSi - a Unified Semantic MT Quality
Evaluation and Estimation Metric for Languages with Different Levels of
Available Resources. Proceedings of the Fourth Conference on
Machine Translation, WMT 2019, Florence, Italy, August 1-2,
2019 - Volume 2: Shared Task Papers, Day 1.
Anais...2019. Disponível em: <https://doi.org/10.18653/v1/w19-5358>
LO, C.; WU, D. MEANT: An inexpensive,
high-accuracy, semi-automatic metric for evaluating translation utility
based on semantic roles. The 49th Annual Meeting of the
Association for Computational Linguistics: Human Language Technologies,
Proceedings of the Conference, 19-24 June, 2011, Portland, Oregon,
USA. Anais...2011. Disponível em: <https://aclanthology.org/P11-1023/>
LO, S. L. et al. Multilingual Sentiment Analysis: From Formal to
Informal and Scarce Resource Languages. Artificial Intelligence
Review, 2017.
LOCATELLI, M. S. et al. Examining the Behavior of LLM
Architectures within the Framework of Standardized National Exams in
Brazil. Proceedings of the 2024 AAAI/ACM Conference on AI,
Ethics, and Society. Anais...AAAI Press, 2025.
LOMMEL, A.; MELBY, A. Tutorial:
MQM-DQF: A Good Marriage (Translation Quality
for the 21st Century). Proceedings of the 13th Conference of
the Association for Machine Translation in the Americas
(Volume 2: User Track). Anais...Boston, MA: Association
for Machine Translation in the Americas, mar. 2018. Disponível em:
<https://aclanthology.org/W18-1925>
LOOI, Z. Q. et al. MeLoDicA AI-Machine Learning Based Detection
of Asthma via Vocal Audio Analysis. 2024 IEEE Conference on
Artificial Intelligence (CAI). Anais...IEEE, 2024.
LOPES, C. DE S. et al. Trend in the prevalence
of depressive symptoms in Brazil: results from the Brazilian National
Health Survey 2013 and 2019. Cad Saude Publica, 6
maio 2022.
LOPES, F.; TEIXEIRA, C.; GONÇALO OLIVEIRA, H. Contributions to
Clinical Named Entity Recognition in Portuguese.
Proceedings of the 18th BioNLP Workshop and Shared Task.
Anais...Florence, Italy: Association for Computational
Linguistics, ago. 2019. Disponível em: <https://www.aclweb.org/anthology/W19-5024>
LÓPEZ, R. et al. A qualitative analysis of a corpus of opinion
summaries based on aspects. Proceedings of the 9th Linguistic
Annotation Workshop. Anais...2015.
LOPEZ-GAZPIO, I. et al. Interpretable semantic textual similarity:
Finding and explaining differences between sentences.
Knowledge-Based Systems, v. 119, p. 186–199, 2017.
LORÈ, F. et al. An
AI framework to support decisions on GDPR compliance.
Journal of Intelligent Information Systems, p. 1–28,
2023.
LOSADA, D. E.; CRESTANI, F. A Test Collection for Research on
Depression and Language Use. Experimental IR Meets
Multilinguality, Multimodality, and Interaction.
Anais...Cham: Springer, 2016.
LOSADA, D. E.; CRESTANI, F.; PARAPAR, J. eRISK 2017: CLEF
lab on early risk prediction on the internet: experimental
foundations. Lecture Notes in Computer Science vol
10456. Anais...Cham: Springer, 2017.
LOSADA, D. E.; CRESTANI, F.; PARAPAR, J. Overview of
eRisk: Early Risk Prediction on the Internet. Lecture
Notes in Computer Science vol 11018. Anais...Cham:
Springer, 2018.
LOSADA, D. E.; CRESTANI, F.; PARAPAR, J. Overview of eRisk
2019 Early Risk Prediction on the Internet. Lecture
Notes in Computer Science vol 11696. Anais...2019.
LOUIS, A.; HIGGINS, D. Off-topic essay detection using short
prompt texts. Proceedings of the NAACL
HLT 2010 Fifth Workshop on Innovative Use of
NLP for Building Educational Applications.
Anais...Los Angeles, California: Association for
Computational Linguistics, jun. 2010.
LOUIS, A.; NENKOVA, A. Automatically assessing machine summary content
without a gold standard. Computational Linguistics, v.
39, n. 2, p. 267–300, 2013.
LOVEYS, K. et al. Small but Mighty: Affective
Micropatterns for Quantifying Mental Health from Social Media
Language. Fourth Workshop on Computational Linguistics and
Clinical Psychology: From Linguistic Signal to Clinical Reality.
Anais...Vancouver, Canada: Association for
Computational Linguistics, 2017.
LOVINS, J. B. Development of a stemming algorithm. Mech. Transl.
Comput. Linguistics, v. 11, n. 1-2, p. 22–31, 1968.
LUCAS, J. et al. Fighting Fire with Fire: The Dual Role of
LLMs in Crafting and Detecting Elusive
Disinformation. (H. Bouamor, J. Pino, K. Bali, Eds.)Proceedings
of the 2023 Conference on Empirical Methods in Natural Language
Processing. Anais...Singapore: Association for
Computational Linguistics, dez. 2023. Disponível em: <https://aclanthology.org/2023.emnlp-main.883>
LUHN, H. P. The automatic creation of literature abstracts. IBM
Journal of research and development, v. 2, n. 2, p. 159–165,
1958.
MA, Q. et al. Blend: a Novel Combined MT Metric
Based on Direct Assessment - CASICT-DCU submission to
WMT17 Metrics Task. Proceedings of the Second
Conference on Machine Translation, WMT 2017, Copenhagen,
Denmark, September 7-8, 2017. Anais...2017. Disponível
em: <https://doi.org/10.18653/v1/w17-4768>
MACDONALD, C.; TONELLOTTO, N. Declarative Experimentation in
Information Retrieval using PyTerrier. Proceedings of ICTIR
2020. Anais...2020.
MACHADO, A. A. A. et al. Personalitatem Lexicon: um léxico em
português brasileiro para mineração de traços de personalidade em
textos. Proceedings of the Brazilian Symposium on Computers in
Education. Anais...2015.
MACHADO, M. T.; PARDO, T. A. S. NILC at
ABSAPT 2022: Aspect Extraction for Portuguese.
Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)
co-located with the Conference of the Spanish Society for Natural
Language Processing (SEPLN 2022), A
Coruña, Spain, September 20, 2022.
Anais...2022.
MACHADO, M. T.; PARDO, T. A. S.; RUIZ, E. E. S. Creating a
portuguese context sensitive lexicon for sentiment analysis.
Proceedings of the 13th international conference on computational
processing of the Portuguese Language (PROPOR).
Anais...2018.
MACIEL, A. M. B. Para
o reconhecimento da especificidade do termo jurídico.
mathesis—[s.l.] Universidade Federal do Rio Grande do Sul, RS, 2001.
MACOHIN, A.; CARNEIRO, J. V. V. Web Crawling e Web Scraping em sites de
tribunais: publicidade processual e proteção de dados pessoais nas
experiências europeia e brasileira. Em: WACHOWICZ, M. (Ed.).
Proteção de Dados Pessoais em Perspectiva: LGPD e RGPD na Ótica
do Direito Comparado. Curitiba: Gedai, UFPR, 2020.
MAIA, D. F. et al. UlyssesSD-Br: Stance Detection
in Brazilian Political Polls. (G. Marreiros et al.,
Eds.)Progress in Artificial Intelligence. Anais...Cham:
Springer International Publishing, 2022. Disponível em: <https://github.com/Dyonnatan/UlyssesSD-Br>
MAIA, M.; LEMLE, M.; FRANÇA, A. I. Efeito stroop e rastreamento ocular
no processamento de palavras. Ciências e Cognição 2007,
v. 12, p. 02–17, 2007.
MALENCHINI, F. M. et al. Um Benchmark para Sistemas de Extração
de Informação Aberta em Português. Proceedings of theSymposium
in Information and Human Language Technology (STIL 2019).
Anais...Salvador, Bahia: SBC, out. 2019.
MALIK, V. et al. ILDC
for CJPE: Indian Legal Documents Corpus for
Court Judgment Prediction and Explanation. Proceedings of
the 59th Annual Meeting of the Association for Computational Linguistics
and the 11th International Joint Conference on Natural Language
Processing (Volume 1: Long Papers). Anais...Association
for Computational Linguistics, 2021.
MALOUF, R. A Comparison of Algorithms for Maximum Entropy
Parameter Estimation. COLING-02: The 6th
Conference on Natural Language Learning 2002
(CoNLL-2002). Anais...2002.
Disponível em: <https://aclanthology.org/W02-2018>
MALTA, D. et al. Prevalence of sexual
violence in school children in Brazil: National School
Health Survey 2019. Population Medicine, v. 5, abr.
2023.
MANDAL, A. et al. Unsupervised
approaches for measuring textual similarity between legal court case
reports. Artificial Intelligence and Law, v. 29, n.
3, p. 417–451, 2021.
MANI, I. Automatic Summarization. John Benjamins Publishing
Company, v. 2, p. 399–408, 2001.
MANI, I.; MAYBURY, M. T. Advances in automatic text
summarization. [s.l.] MIT press, 1999.
MANN, P.; MATSUSHIMA, E. H.; PAES, A. Detecting
Depression from Social Media Data as a Multiple-Instance Learning
Task. 10th International Conference on Affective Computing
and Intelligent Interaction (ACII). Anais...2022.
MANN, P.; PAES, A.; MATSUSHIMA, E. H. See and Read: Detecting
Depression Symptoms in Higher Education Students Using Multimodal Social
Media Data. Proceedings of the International AAAI Conference on
Web and Social Media. Anais...2020.
MANN, W.; THOMPSON, S. Rethorical Structure
Theory: Toward a functional theory of text organization.
Text, v. 8, p. 243–281, jan. 1988.
MANNING, C. D.; SCHÜTZE, H.; RAGHAVAN, P. Introduction to
information retrieval. [s.l.] Cambridge University Press
Cambridge, 2008.
MARCUSCHI, L. A. Produção textual,
análise de gêneros e
compreensão. [s.l.] Parábola Ed.,
2008.
MARGARIDO, P. R. A. et al. Automatic Summarization for Text
Simplification: Evaluating Text Understanding by Poor Readers.
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and
the Web. Anais...: WebMedia ’08.New York, NY, USA: ACM,
2008. Disponível em: <http://doi.acm.org/10.1145/1809980.1810057>
MARIE, B.; FUJITA, A.; RUBINO, R. Scientific
Credibility of Machine
Translation Research: A
Meta-Evaluation of 769
Papers. arXiv:2106.15195 [cs], jun.
2021.
MARINHO, J. et al. Automated Essay Scoring: An approach based on
ENEM competencies. Anais do XIX Encontro Nacional de
Inteligência Artificial e Computacional. Anais...SBC,
a2022.
MARINHO, J.; ANCHIÊTA, R.; MOURA, R. Essay-BR: a Brazilian Corpus to
Automatic Essay Scoring Task. Journal of Information and Data
Management, v. 13, n. 1, p. 65–76, b2022.
MARTINS, D. B. DE J. Pós-edição automática de textos traduzidos
automaticamente de inglês para português do Brasil.
Mestrado—São Carlos: Universidade Federal de São Carlos, 2014.
MARTINS, D. B. DE J.; CASELI, H. DE M. Automatic machine
translation error identification. Machine
Translation, v. 29, n. 1, p. 1–24, 2015.
MARTINS, R. T. et al. An interlingua aiming at communication on
the Web: How language-independent can it be?
NAACL-ANLP 2000 Workshop: Applied
Interlinguas: Practical Applications of Interlingual Approaches to
NLP. Anais...2000. Disponível em: <https://aclanthology.org/W00-0204>
MARTINS, T. B. F. et al. Readability Formulas Applied to
Textbooks in Brazilian Portuguese. [s.l.] ICMSC-USP, 1996.
MASCARENHAS, M. D. M. et al. Análise das
notificações de violência por parceiro íntimo contra mulheres, Brasil,
2011-2017. Revista Brasileira de Epidemiologia, v.
23, p. e200007.SUPL.1, 2020.
MATTEI, L. D. et al. ATE ABSITA@ EVALITA2020: Overview of the Aspect
Term Extraction and Aspect-based Sentiment Analysis Task.
Proceedings of the 7th Evaluation Campaign of Natural Language
Processing and Speech tools for Italian (EVALITA 2020), 2020.
MATTHIESSEN, M. C. M. I. Applying systemic
functional linguistics in healthcare contexts. Text and
Talk, v. 33, n. 4-5, p. 437–447, 19 ago. 2013.
MATTHIESSEN, M. C. M. I.; TERUYA, K.; WU, C. Multilingual studies as a
multi-dimensional space of interconnected language studies. Em:
Meaning in context : strategies for implementing intelligent
applications of language studies. [s.l.] Continuum, 2008. p.
146–221.
MAX, A. Writing
for Language-Impaired Readers. In: Gelbukh A. (eds)
Computational Linguistics and Intelligent Text Processing. CICLing 2006.
Lecture Notes in Computer Science, vol 3878.
Anais...Springer, Berlin, Heidelberg, 2006.
MAXWELL, K. T.; SCHAFER, B. Concept and context
in legal information retrieval. Em: Legal Knowledge and
Information Systems. [s.l.] IOS Press, 2008. p. 63–72.
MAYER, R. E. Elaboration techniques that increase the meaningfulness of
technical text: An experimental test of the learning strategy
hypothesis. Journal of Educational Psychology, v. 72,
n. 6, p. 770–784, 1980.
MAYFIELD, E.; BLACK, A. W. Should You Fine-Tune
BERT for Automated Essay Scoring? Proceedings of
the Fifteenth Workshop on Innovative Use of NLP for
Building Educational Applications. Anais...Association
for Computational Linguistics, jul. 2020.
MAZIERO, E. G.; PARDO, T. A. S.; ALUÍSIO, S. M. Ferramenta de Análise
Automática de Inteligibilidade de Córpus (AIC). NILC -
ICMC-USP, 2008.
MCCALLUM, A.; LI, W. Early results for named entity recognition
with conditional random fields, feature induction and web-enhanced
lexicons. Proceedings of the seventh conference on Natural
language learning at HLT-NAACL 2003-Volume 4.
Anais...2003.
MCNAMARA, D. S. et al. Coh-Metrix Common Core T.E.R.A. version
1.0., 2013. Disponível em: <http://www.commoncoretera.com/>
MCNAMARA, D. S. et al. Automated Evaluation of Text and
Discourse with Coh-Metrix. 1a. ed. [s.l.] Cambridge University
Press, 2014.
MCWHORTER, J. H. The
Worlds Simplest Grammars Are Creole Grammars. Linguistic
Typology, v. 5, n. 2-3, jan. 2001.
MENDES, A. R.; CASELI, H. M. Identifying Fine-grained Depression
Signs in Social Media Posts. Proceedings of the 2024 joint
international conference on computational linguistics, language
resources and evaluation (LREC 2024). Anais...2024.
MEYER, C. F. et al. The world wide web as linguistic corpus. Em:
Corpus Analysis. [s.l.] Brill Rodopi, 2003. p. 241–254.
MIKOLOV, T. et al. Distributed Representations of Words and
Phrases and their Compositionality. (C. J. Burges et al.,
Eds.)Advances in Neural Information Processing Systems.
Anais...Curran Associates, Inc., 2013. Disponível em:
<https://proceedings.neurips.cc/paper_files/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf>
MILLER, G. A. WordNet: A Lexical Database for English.
Communications of the ACM, v. Vol. 38, No. 11, p.
39–41, 1995.
MIWA, M.; BANSAL, M. End-to-End Relation Extraction using LSTMs
on Sequences and Tree Structures. Proceedings of the 54th
Annual Meeting of the Association for Computational Linguistics (Volume
1: Long Papers). Anais...Association for Computational
Linguistics, 2016.
MOHAN, S. et al. The Impact of Toxic Language on the Health of
Reddit Communities. Proceedings of the Canadian Conference on
AI. Anais...2017.
MOLLAS, I. et al. ETHOS: a multi-label hate speech
detection dataset. Complex & Intelligent Systems,
2022.
MONTEIRO, R. A. et al. Contributions to the Study of Fake News
in Portuguese: New Corpus and Automatic Detection Results.
Proceedings of the 13th international conference on computational
processing of the Portuguese Language. Anais...Canela,
Rio Grande do Sul, Brazil: Springer International Publishing, set. 2018.
MONTORO, A. F. Curso de Teoria Geral do Direito - Aula 2: A
linguagem do direito: semântica, sintática e pragmática.
Disponível em: <http://www.dialdata.com.br/ilam/aula2>.
MOORKENS, J. et al. Correlations of perceived
post-editing effort with measurements of actual effort.
Machine Translation, v. 29, n. 3/4, p. 267–284, 2015.
MOORKENS, J. Under pressure:
translation in times of austerity. Perspectives, v.
25, n. 3, p. 464–477, fev. 2017.
MORENO, G. C. DE L. et al. ALT: um software para analise de
legibilidade de textos em Lingua Portuguesa.
preprint, 2022.
MORENO, J.; BRESSAN, G. FACTCK.BR: a new dataset to
study fake news. : WebMedia ’19.New York, NY, USA: Association
for Computing Machinery, 2019. Disponível em: <https://doi.org/10.1145/3323503.3361698>
MORENO SCHNEIDER, J. et al. Lynx: A knowledge-based
AI service platform for content processing, enrichment and analysis for
the legal domain. Information Systems, v. 106, p.
101966, 2022.
MOTA, C. R3M, uma participação minimalista no
Segundo HAREM. quot; In Cristina Mota; Diana Santos (ed)
Desafios na avaliação conjunta do
reconhecimento de entidades mencionadas: O Segundo HAREM Linguateca
2008, 2008.
MOTA, C. C. et al. Reconhecimento de
entidades nomeadas em documentos jurı́dicos em
português utilizando redes neurais. Encontro
Nacional de Inteligência Artificial e Computacional
(ENIAC). Anais...SBC, 2021.
MOTA, C.; SANTOS, D. (EDS.). Desafios na avaliação conjunta
do reconhecimento de entidades mencionadas: O Segundo
HAREM. [s.l.] Linguateca, 2008.
MOTA, C.; SANTOS, D.; RANCHHOD, E. Avaliação
de reconhecimento de entidades mencionadas: princı́pio de
HAREM. Avaliação
conjunta: um novo paradigma no processamento computacional da
lı́ngua portuguesa, p. 161–175, 2007.
MOTTA, E. Sentenças
Judiciais e Acessibilidade Textual e Terminológica. Domínios
de Lingu@gem, v. 15, n. 3, p. 761–813, 2021.
MOTTA, E. SENTENÇAS JUDICIAIS
E LINGUAGEM SIMPLES: um encontro possível e necessário.
mathesis—[s.l.] Universidade Federal do Rio Grande do Sul, RS, 2022.
MOUAWAD, P.; DUBNOV, T.; DUBNOV, S. Robust detection of COVID-19 in
cough sounds: using recurrence dynamics and variable Markov model.
SN Computer Science, v. 2, n. 1, p. 34, 2021.
MOUJAHID, H. et al. Combining CNN and Grad-Cam for COVID-19 Disease
Prediction and Visual Explanation. Intelligent Automation &
Soft Computing, v. 32, n. 2, 2022.
NADEAU, D. Semi-Supervised
Named Entity Recognition: Learning to Recognize 100 Entity Types with
Little Supervision. tese de doutorado—[s.l.] University of
Ottawa, 2007.
NAGAO, M. A
Framework of a Mechanical
Translation between Japanese and
English by Analogy Principle.
Em: NIRENBURG, S.; SOMERS, H. L.; WILKS, Y. A. (Eds.). Readings
in Machine Translation. [s.l.] The
MIT Press, 1984.
NAIR, S. S.; JEEVEN, V. A brief overview of metadata formats.
DESIDOC Journal of Library & Information
Technology, v. 24, n. 4, 2004.
NAIR, V.; HINTON, G. E. Rectified linear units improve
restricted boltzmann machines. Icml.
Anais...2010.
NARDE, W. Análise de notícias falsas em rede social: uma
abordagem utilizando transferência de aprendizagem e
Transformers. https://www.monografias.ufop.br/bitstream/35400000/3122/6/MONOGRAFIA_AnáliseNotíciasFalsas.pdf,
2021.
NASAR, Z.; JAFFRY, S. W.; MALIK, M. K. Named entity recognition and
relation extraction: State-of-the-art. ACM Computing Surveys
(CSUR), v. 54, n. 1, p. 1–39, 2021.
NASCIMENTO, D. N. C. R. DO. Sumarização de artigos científicos
em português no domínio da saúde. mathesis—[s.l.] (Mestrado em
Informática) - Programa de Pós-Graduação em Informática da PUC-Rio, Rio
de Janeiro, 2023.
NASCIMENTO, G. et al. Hate speech detection using brazilian
imageboards. Proceedings of the 25th Brazillian Symposium on
Multimedia and the Web. Anais...2019.
NASCIMENTO, R. DA S. et al. Identificando Sinais de
Comportamento Depressivo em Redes Sociais. Anais do VII
Brazilian Workshop on Social Network Analysis and Mining.
Anais...Porto Alegre, Brazil: SBC, 2018.
NATH, N.; LEE, S.-H.; LEE, I. NEAR: Named Entity and
Attribute Recognition of Clinical Concepts. J. of Biomedical
Informatics, v. 130, n. C, jun. 2022.
NECO, R. P.; FORCADA, M. L. Asynchronous
translations with recurrent neural nets. Proceedings of
International Conference on Neural Networks (ICNN’97).
Anais...1997.
NETO, J. R. C. S. A. V. S.; FALEIROS, T. DE P. Deep Active-Self
Learning Applied to Named Entity Recognition. (A. Britto, K.
Valdivia Delgado, Eds.)Intelligent Systems.
Anais...Cham: Springer International Publishing, 2021.
Disponível em: <https://avio11.github.io/resources/aposentadoria/aposentadoria.html>
NEURALMIND. NeuralMind disponibiliza modelo BERT do Google em
português. Neuralmind blog. Disponível em: <https://neuralmind.ai/2020/01/26/neuralmind-disponibiliza-modelo-bert-inteligencia-artificial-do-google-em-portugues/>.
NEWELL, E. et al. Assessing the Verifiability of Attributions in
News Text. (G. Kondrak, T. Watanabe, Eds.)Proceedings of the
Eighth International Joint Conference on Natural Language Processing
(Volume 1: Long Papers). Anais...Taipei, Taiwan: Asian
Federation of Natural Language Processing, nov. 2017. Disponível em:
<https://aclanthology.org/I17-1076>
NEWMAN, N. et al. Reuters institute digital news report
2020. [s.l.] Report of the Reuters Institute for the Study of
Journalism, 2020.
NGUYEN, D. B.; THEOBALD, M.; WEIKUM, G. J-NERD: joint named entity
recognition and disambiguation with rich linguistic features.
Transactions of the Association for Computational
Linguistics, v. 4, p. 215–229, 2016.
NICHOLS, J. Linguistic Diversity in Space and Time.
[s.l.] University of Chicago Press, 1998.
NIIZUMI, D. et al. Masked spectrogram modeling using masked
autoencoders for learning general-purpose audio representation.
HEAR: Holistic Evaluation of Audio Representations.
Anais...PMLR, 2022.
NIIZUMI, D. et al. Masked modeling duo: Learning representations
by encouraging both networks to model the input. ICASSP
2023-2023 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). Anais...IEEE, 2023.
NIIZUMI, D. et al. Towards Pre-training an Effective Respiratory Audio
Foundation Model. arXiv preprint arXiv:2505.15307,
2025.
NOGUEIRA, R. et al. Document expansion by query prediction.
arXiv preprint arXiv:1904.08375, 2019.
NUNES, M. DAS G. V. et al. O uso de interlíngua para
comunicação via Internet: a
decodificação UNL-português.
Revista Tecnologia da
Informação, v. 3, n. 1, p. 49–55,
2003.
NUNES, R. O. et al. Out of Sesame Street: A
Study of Portuguese Legal Named Entity Recognition Through In-Context
Learning. INSTICC; SciTePress, a2024.
NUNES, R. O. et al. A Named Entity Recognition Approach for
Portuguese Legislative Texts Using Self-Learning.
(P. Gamallo et al., Eds.)Proceedings of the 16th International
Conference on Computational Processing of Portuguese - Vol. 1.
Anais...Santiago de Compostela, Galicia/Spain:
Association for Computational Lingustics, mar. b2024. Disponível em:
<https://aclanthology.org/2024.propor-1.30>
O’BRIEN, S. Towards predicting post-editing productivity.
Machine translation, v. 25, p. 197–215, 2011.
O’BRIEN, S. et al. Dynamic Quality
Evaluation Framework. [s.l.] TAUS
Labs Report. The Translation Automation User Society-TAUS, 2011.
OCH, F. J.; NEY, H. The Alignment Template
Approach to Statistical Machine Translation. Computational
Linguistics, v. 30, n. 4, p. 417–449, dez. 2004.
ÖHMAN, A. et al. The public health turn on violence against women:
analysing Swedish healthcare law, public health and gender-equality
policies. BMC Public Health, v. 20, p. 1–12, 2020.
OKANO, E. Y. et al. Fake News Detection on Fake.Br Using
Hierarchical Attention Networks. (P. Quaresma et al.,
Eds.)Computational Processing of the Portuguese Language.
Anais...Cham: Springer International Publishing, 2020.
OKANO, E. Y.; RUIZ, E. E. S. Using linguistic cues to detect
fake news on the brazilian portuguese parallel corpus Fake.br.
Proceedings of the Symposium in Information and Human Language
Technology. Anais...Brazilian Computer Society, 2019.
OKSANEN, A. et al. Semantic
Finlex: Transforming, Publishing, and Using Finnish Legislation and Case
Law As Linked Open Data on the Web. Em: PERUGINELLI, G.; FARO, S.
(Eds.). Knowledge of the Law in the Big Data Age.
Frontiers em Artificial Intelligence e Applications. [s.l.] IOS Press,
2019a. v. 317p. 212–228.
OKSANEN, A. et al. ANOPPI: A Pseudonymization
Service for Finnish Court Documents. JURIX.
Anais...b2019.
OKSANEN, A. et al. An Anonymization Tool for Open Data
Publication of Legal Documents. Joint Proceedings of ISWC2022
Workshops. Anais...CEUR-WS. org, 2022. Disponível em:
<https://ceur-ws.org/Vol-3257/>
OLIVAL, F.; CAMERON, H.; VIEIRA, R. As Memórias Paroquiais: do
manuscrito ao digital. Actas da Jornada de Humanidades Digitais
do CIDEHUS (to appear). Anais...2022.
OLIVEIRA, I. L. Uma
mentira repetida mil vezes se transforma em verdade? Reflexões sobre as
dinâmicas discursivas e seus efeitos na saúde. Em:
Desinformação o mal do século: Distorções, inverdades, fake
news: a democracia ameaçada. [s.l: s.n.]. p. 299–315.
OLIVEIRA, L. E. S. et al. SemClinBr
- a multi-institutional and multi-specialty semantically annotated
corpus for Portuguese clinical NLP tasks.
Journal of Biomedical Semantics, v. 13, n. 1, a2022.
OLIVEIRA, L. F. A. DE et al. Challenges In Annotating A Treebank
Of Clinical Narratives In Brazilian Portuguese. Computational
Processing of the Portuguese Language: 15th International Conference,
PROPOR 2022, Fortaleza, Brazil, March 21–23, 2022, Proceedings.
Anais...Berlin, Heidelberg: Springer-Verlag, b2022.
Disponível em: <https://doi.org/10.1007/978-3-030-98305-5_9>
OLIVEIRA, L.; CLARO, D.; SOUZA, M. DptOIE: a Portuguese
open information extraction based on dependency analysis.
Artificial Intelligence Review, v. 56, p. 1–32, dez.
c2022.
OLIVEIRA, N. et al. Processamento de Linguagem Natural para
Identificação de Notícias Falsas em Redes Sociais: Ferramentas,
Tendências e Desafios. Em: [s.l.] SBC, 2020.
OLIVEIRA, R. L. DE; MARTINS, J. T.; PARABONI, I. Mental health
prediction from social media connections. New Review of
Hypermedia and Multimedia, a2024.
OLIVEIRA, R. L. DE; PARABONI, I. A Bag-of-Users approach
to mental health prediction from social media data.
16th International Conference on Computational Processing of
Portuguese (PROPOR 2024). Anais...Santiago de
Compostela, Spain: 2024.
OLIVEIRA, V. et al. Combining prompt-based
language models and weak supervision for labeling named entity
recognition on legal documents. Artificial Intelligence and
Law, p. 1–21, fev. b2024.
ONAGA, T.; FUJITA, M.; YOSHINOBU, K. Japanese Legal Bar Problem
Solver Focusing on Person Names. Proceedings of the Tenth
International Competition on Legal Information Extraction/Entailment
(COLIEE 2023). Anais...2023. Disponível em: <https://sites.ualberta.ca/~rabelo/COLIEE2023>
ORENGO, V. M.; BURIOL, L. S.; COELHO, A. R. A study on the use
of stemming for monolingual ad-hoc Portuguese information
retrieval. Workshop of the Cross-Language Evaluation Forum for
European Languages. Anais...Springer, 2006.
ORENGO, V. M.; HUYCK, C. A Stemming Algorithmm
for the Portuguese Language. Proceedings Eighth Symposium
on String Processing and Information Retrieval.
Anais...IEEE Computer Society, 2001.
ORLANDIC, L.; TEIJEIRO, T.; ATIENZA, D. The COUGHVID crowdsourcing
dataset, a corpus for the study of large-scale cough analysis
algorithms. Scientific Data, v. 8, n. 1, p. 156, 2021.
OSTENDORFF, M. et al. Evaluating document representations for
content-based legal literature recommendations. Proceedings of
the Eighteenth International Conference on Artificial Intelligence and
Law. Anais...2021. Disponível em: <https://doi.org/10.1145/3462757.3466073>
OTT, M. et al. Finding Deceptive Opinion Spam by Any Stretch of
the Imagination. (D. Lin, Y. Matsumoto, R. Mihalcea,
Eds.)Proceedings of the 49th Annual Meeting of the Association for
Computational Linguistics: Human Language Technologies.
Anais...Portland, Oregon, USA: Association for
Computational Linguistics, jun. 2011. Disponível em: <https://aclanthology.org/P11-1032>
PACK, A.; BARRETT, A.; ESCALANTE, J. Large language models
and automated essay scoring of English language learner writing:
Insights into validity and reliability. Computers and
Education: Artificial Intelligence, v. 6, p. 100234, 2024.
PAETZOLD, G. H.; SPECIA, L. Unsupervised Lexical Simplification
for Non-native Speakers. Proceedings of the Thirtieth AAAI
Conference on Artificial Intelligence. Anais...:
AAAI’16.Phoenix, Arizona: AAAI Press, a2016. Disponível em: <http://dl.acm.org/citation.cfm?id=3016387.3016433>
PAETZOLD, G.; SPECIA, L. Inferring Psycholinguistic Properties
of Words. NAACL HLT 2016, The 2016
Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, San Diego
California, USA, June 12-17, 2016. Anais...b2016.
Disponível em: <http://aclweb.org/anthology/N/N16/N16-1050.pdf>
PAETZOLD, G.; SPECIA, L. Understanding the Lexical
Simplification Needs of Non-Native Speakers of
English. (Y. Matsumoto, R. Prasad,
Eds.)Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: Technical Papers.
Anais...Osaka, Japan: The COLING 2016 Organizing
Committee, dez. c2016. Disponível em: <https://aclanthology.org/C16-1069>
PAETZOLD, G.; SPECIA, L. Lexical Simplification with Neural
Ranking. Proceedings of the 15th Conference of the European
Chapter of the Association for Computational Linguistics: Volume 2,
Short Papers. Anais...Valencia, Spain: Association for
Computational Linguistics, abr. 2017. Disponível em: <http://www.aclweb.org/anthology/E17-2006>
PAIOLA, P. H. Sumarização abstrativa de textos em português
utilizando aprendizado de máquina. mathesis—[s.l.] (Mestrado em
Ciências da Computação) - Programa de Pós-Graduação em Ciência da
Computação, Universidade Estadual Paulista "Júlio de Mesquita Filho",
2022.
PAIOLA, P. H. et al. RecognaSumm: A
Novel Brazilian Summarization Dataset. (P. Gamallo
et al., Eds.)Proceedings of the 16th International Conference on
Computational Processing of Portuguese - Vol. 1.
Anais...Santiago de Compostela, Galicia/Spain:
Association for Computational Lingustics, mar. 2024. Disponível em:
<https://aclanthology.org/2024.propor-1.63>
PAIOLA, P. H.; ROSA, G. H. DE; PAPA, J. P. Deep Learning-Based
Abstractive Summarization for Brazilian Portuguese Texts.
Intelligent Systems: 11th Brazilian Conference, BRACIS 2022, Campinas,
Brazil, November 28 – December 1, 2022, Proceedings, Part II.
Anais...Berlin, Heidelberg: Springer-Verlag, 2022.
Disponível em: <https://doi.org/10.1007/978-3-031-21689-3_34>
PAIS, V. et al. Named Entity
Recognition in the Romanian Legal Domain.
Proceedings of the Natural Legal Language Processing Workshop 2021.
Anais...Punta Cana, Dominican Republic: Association for
Computational Linguistics, nov. 2021.
PAIS, V. et al. LegalNERo: A
linked corpus for named entity recognition in the Romanian legal
domain. Semantic Web journal, 2024.
PANWAR, H. et al. A deep learning and grad-CAM based color visualization
approach for fast detection of COVID-19 cases using chest X-ray and
CT-Scan images. Chaos, Solitons & Fractals, v. 140,
p. 110190, 2020.
PAPA, J. P.; FALCÃO, A. X.; SUZUKI, C. T. N. Supervised pattern
classification based on optimum-path forest. International
Journal of Imaging Systems and Technology, v. 19, n. 2, p.
120–131, 2009.
PAPINENI, K. et al. BLEU: A Method for Automatic
Evaluation of Machine Translation. Proceedings of the
40th Annual Meeting on Association for Computational Linguistics.
Anais...: ACL ’02.USA: Association for Computational
Linguistics, 2002. Disponível em: <https://doi.org/10.3115/1073083.1073135>
PARAGUASSU, L. et al. MedSimples: An Automated Simplification
Tool for Promoting Health Literacy in Brazil. DHandNLP@PROPOR.
Anais...2020. Disponível em: <https://api.semanticscholar.org/CorpusID:218910691>
PARDO, T. A. S. Gistsumm: Um sumarizador automático
baseado na ideia principal de textos. [s.l.] Série de
Relatórios do Núcleo Interinstitucional de Linguística Computacional,
Universidade de São Paulo, 2002.
PARDO, T. A. S.; RINO, L. H. M. TeMário: Um corpus
para sumarização automática de
textos. [s.l.] Série de Relatórios
Técnicos da Universidade de São Carlos, 2003.
PARIDA, S.; MOTLICEK, P. Abstract text summarization: A low
resource challenge. Proceedings of the 2019 Conference on
Empirical Methods in Natural Language Processing and the 9th
International Joint Conference on Natural Language Processing
(EMNLP-IJCNLP). Anais...2019.
PARK, S. et al. Activities on Facebook Reveal the Depressive State of
Users. J Med Internet Res, v. 15, n. 10, p. e217, 2013.
PAROUBEK, P.; CHAUDIRON, S.; HIRSCHMAN, L. Principles of
Evaluation in Natural Language Processing. Traitement
Automatique des Langues, Volume 48, Numéro 1 : Principes de
l’évaluation en Traitement Automatique des Langues
[Principles of Evaluation in Natural Language Processing].
Anais...France: ATALA (Association pour le Traitement
Automatique des Langues), 2007. Disponível em: <https://aclanthology.org/2007.tal-1.1>
PASQUALINI, B. Corpop : um corpus de referência do português
popular escrito do Brasil. UFRGS - Porto Alegre - RS: Instituto
de Letras - UFRGS, 2018.
PASQUALOTTI, P. R. WordNet Affect BR – uma base de expressões de
emoção em Português. [s.l.] Novas Edições Acadêmicas, 2015.
PELLE, R. P. DE; MOREIRA, V. Offensive Comments in the Brazilian
Web: a dataset and baseline results. Anais do VI Brazilian
Workshop on Social Network Analysis and Mining.
Anais...2017.
PENNEBAKER, J. W. et al. The development and psychometric properties of
LIWC2015. The University of Texas at Austin, 2015.
PENNEBAKER, J. W.; FRANCIS, M. E.; BOOTH, R. J. Linguistic
Inquiry and Word Count. [s.l.] Lawerence Erlbaum Associates,
2001.
PENNINGTON, J.; SOCHER, R.; MANNING, C.
GloVe: Global Vectors for Word
Representation. Proceedings of the 2014 Conference on Empirical
Methods in Natural Language Processing (EMNLP).
Anais...Doha, Qatar: Association for Computational
Linguistics, out. 2014. Disponível em: <https://aclanthology.org/D14-1162>
PEREIRA, D. A. A Survey of Sentiment Analysis in the Portuguese
Language. Artificial Intelligence Review, 2021.
PEREIRA, V.; PINHEIRO, V. Report - um sistema de
extração de
informações aberta para língua
portuguesa. Anais do X Simpósio Brasileiro de
Tecnologia da Informação e da Linguagem
Humana. Anais...SBC, 2015.
PÉREZ-ROSAS, V.; MIHALCEA, R. Cross-cultural Deception
Detection. Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics.
Anais...Baltimore, MD, USA: Association for
Computational Linguistics, 2014.
PERSING, I.; NG, V. Modeling Prompt Adherence in Student
Essays. Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics.
Anais...Baltimore, Maryland: Association for
Computational Linguistics, jun. 2014.
PETERS, M. E. et al. Semi-supervised sequence tagging with
bidirectional language models. Proc. of ACL-2017.
Anais...Vancouver, Canada: Association for
Computational Linguistics, 2017.
PETERS, M. E. et al. Deep Contextualized Word
Representations. (M. A. Walker, H. Ji, A. Stent,
Eds.)Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA,
June 1-6, 2018, Volume 1 (Long Papers).
Anais...Association for Computational Linguistics,
2018. Disponível em: <https://doi.org/10.18653/v1/n18-1202>
PETRI, M. J. C. Manual de Linguagem Jurídica. 3rd. ed.
São Paulo: Saraiva, 2017.
PILÁN, I. et al. The text anonymization benchmark (tab): A dedicated
corpus and evaluation framework for text anonymization.
Computational Linguistics, v. 48, n. 4, p. 1053–1101,
2022.
PINKAS, G. et al. SARS-CoV-2 Detection
From Voice. IEEE Open Journal of Engineering in Medicine and
Biology, v. 1, p. 268–274, 2020.
PINTO, I. V. et al. Fatores
associados ao óbito de mulheres com notificação de violência por
parceiro íntimo no Brasil. Ciência & Saúde
Coletiva, v. 26, n. 3, p. 975–985, mar. 2021.
PIRES, R. et al. Sabiá: Portuguese Large Language
Models. (M. C. Naldi, R. A. C. Bianchi, Eds.)Intelligent
Systems. Anais...Cham: Springer Nature Switzerland,
2023.
PIRES, V.; SILVA, D. G. E. Portuguese Fake News Classification
with BERT models. Anais do XXI Encontro Nacional de
Inteligência Artificial e Computacional. Anais...Porto
Alegre, RS, Brasil: SBC, 2024. Disponível em: <https://sol.sbc.org.br/index.php/eniac/article/view/33848>
PIRINA, I.; ÇÖLTEKIN, ÇAĞRI. Identifying Depression on
Reddit: The Effect of Training Data. Proceedings
of the 2018 EMNLP Workshop SMM4H:
The 3rd Social Media Mining for Health Applications Workshop
& Shared Task. Anais...2018.
PITLER, E.; LOUIS, A.; NENKOVA, A. Automatic evaluation of
linguistic quality in multi-document summarization. Proceedings
of the 48th Annual Meeting of the Association for Computational
Linguistics. Anais...2010.
POLO, F. M. et al. LegalNLP – Natural Language Processing
methods for the Brazilian Legal Language., 2021. Disponível em:
<https://arxiv.org/abs/2110.15709>
PONTES, L. B. L.; OLIVEIRA, H. T. A. DE; ASSIS BOLDT, F. DE.
Avaliação de Modelos Neurais para
Sumarização de
Código-fonte. Anais do XLIX Seminário
Integrado de Software e Hardware. Anais...SBC, 2022.
PONTIKI, M. et al. SemEval-2014 Task
4: Aspect Based Sentiment Analysis. Proceedings of the 8th
International Workshop on Semantic Evaluation (SemEval 2014).
Anais...Association for Computational Linguistics,
2014. Disponível em: <https://aclanthology.org/S14-2004/>
PONTIKI, M. et al. SemEval-2015 Task
12: Aspect Based Sentiment Analysis. Proceedings of the 9th
International Workshop on Semantic Evaluation.
Anais...2015.
PONTIKI, M. et al. SemEval-2016 Task
5: Aspect Based Sentiment Analysis. Proceedings of the 10th
International Workshop on Semantic Evaluation
(SemEval-2016). Anais...2016.
POPOVIC, M.; BURCHARDT, A. From Human to Automatic Error
Classification for Machine Translation Output. Proceedings of
the 15th Conference of the European Association for Machine Translation.
Anais...Leuven, Belgium: 2011. Disponível em: <https://aclanthology.org/2011.eamt-1.36.pdf>
POPOVIĆ, M. chrF: character n-gram
F-score for automatic MT evaluation.
Proceedings of the Tenth Workshop on Statistical Machine Translation.
Anais...Lisbon, Portugal: Association for Computational
Linguistics, set. 2015. Disponível em: <https://aclanthology.org/W15-3049>
PORTER, M. F. An algorithm
for suffix stripping. Program, v. 14, n. 3, p.
130–137, 1980.
PRADEEP, R. et al. H2oloo
at trec 2020: When all you got is a hammer... deep learning, health
misinformation, and precision medicine. Corpus, v.
5, n. d3, p. d2, 2020.
PUSTEJOVSKY, J. The Generative Lexicon. Cambridge, USA:
MIT Press, 1995.
QIU, H. et al. AMRFact: Enhancing Summarization
Factuality Evaluation with AMR-Driven Negative Samples
Generation. (K. Duh, H. Gomez, S. Bethard, Eds.)Proceedings of
the 2024 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies (Volume 1: Long
Papers). Anais...Mexico City, Mexico: Association for
Computational Linguistics, jun. 2024. Disponível em: <https://aclanthology.org/2024.naacl-long.33/>
QIU, Q. et al. BiLSTM-CRF for geological named entity recognition from
the geoscience literature. Earth Science Informatics,
v. 12, n. 4, p. 565–579, 2019.
QUARESMA, P.; FINATTO, M. J. B. Information Extraction from
Historical Texts: a Case Study. DHandNLP@ PROPOR.
Anais...2020.
QUARESMA, P.; GONÇALVES, T. Using Linguistic
Information and Machine Learning Techniques to Identify Entities from
Juridical Documents. Em: FRANCESCONI, E. et al. (Eds.).
Semantic Processing of Legal Texts: Where the Language of Law
Meets the Law of Language. Berlin, Heidelberg: Springer Berlin
Heidelberg, 2010. p. 44–59.
RADEV, D. R. A common theory of information fusion from multiple
text sources step one: cross-document structure. 1st SIGdial
workshop on Discourse and Dialogue. Anais...2000.
RAFFEL, C. et al. Exploring the Limits of
Transfer Learning with a Unified Text-to-Text Transformer.
Journal of Machine Learning Research, v. 21, n. 140, p.
1–67, 2020.
RAMISCH, R. Caracterização de desvios
sintáticos em redações de
estudantes do ensino médio: subsídios para o
processamento automático das línguas
naturais. mathesis—[s.l.] Universidade Federal de
São Carlos, 2020.
RANA, M. S. et al. Deepfake Detection: A
Systematic Literature Review. IEEE Access, v. 10,
p. 25494–25513, 2022.
RAU, L. F. Extracting company names from text.
Proceedings the Seventh IEEE Conference on Artificial Intelligence
Application. Anais...IEEE Computer Society, 1991.
RAYNER, K. Eye Movements in Reading and Information Processing: 20 Years
of Research. Psychological Bulletin - APA, vol. 124 n.
3, p. 372–422, 1998.
RECUERO, R. Redes Sociais na Internet. [s.l.] Ciber
Cultura, 2009.
REI, R. et al. COMET: A Neural Framework for
MT Evaluation. Proceedings of the 2020 Conference
on Empirical Methods in Natural Language Processing (EMNLP).
Anais...Online: Association for Computational
Linguistics, nov. 2020. Disponível em: <https://aclanthology.org/2020.emnlp-main.213>
REIS, G. B. Predição da Complexidade Textual de Notícias Jornalísticas
usando uma Plataforma Crowdsourcing. Monografia Conclusão Curso
- USP, 2017.
RESENDE, G. et al. (Mis)Information Dissemination in WhatsApp:
Gathering, Analyzing and Countermeasures. Proceedings of the
World Wide Web Conference. Anais...2019.
REYES, A.; ROSSO, P.; BUSCALDI, D. From Humor Recognition to Irony
Detection: The Figurative Language of Social Media. Data &
Knowledge Engineering, 2012.
RIBEIRO, A. S. O projecto MONSOON: perspectivas digitais da
Índia portuguesa. Actas da Jornada de Humanidades Digitais do
CIDEHUS (to appear). Anais...2022.
RICARTE NETO, F. A. et al. Team PiLN at ABSAPT 2022: Lexical and
BERT Strategies for Aspect-Based Sentiment Analysis in
Portuguese. Proceedings of the Iberian Languages Evaluation
Forum (IberLEF 2022) co-located with the Conference of the Spanish
Society for Natural Language Processing (SEPLN 2022),
A Coruña, Spain, September 20, 2022.
Anais...2022.
RICKARD, E. M. S. A. M. L. K. A. B. D. F. A. N. S. Predicting Depression From
Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and
Twitter Status Updates. Journal of Medical Internet
Research, v. 20, n. 5, p. e168, 2018.
RIJSBERGEN, C. JOOST. VAN. Information Retrieval.
[s.l.] Butterworths, 1979.
RILOFF, E. et al. Automatically constructing a dictionary for
information extraction tasks. AAAI.
Anais...Citeseer, 1993.
RILOFF, E.; JONES, R.; et al. Learning dictionaries for
information extraction by multi-level bootstrapping. AAAI/IAAI.
Anais...1999.
RINO, L. H. M.; PARDO, T. A. S. A
Sumarização Automática de textos:
principais caracterı́sticas e metodologias. Anais
do XXIII Congresso da Sociedade Brasileira de
Computação. Anais...2003.
RO, Y.; LEE, Y.; KANG, P.
Multi^2OIE: Multilingual
Open Information Extraction Based on Multi-Head Attention with
BERT. Findings of the Association for
Computational Linguistics: EMNLP 2020. Anais...Online:
Association for Computational Linguistics, nov. 2020. Disponível em:
<https://aclanthology.org/2020.findings-emnlp.99>
ROARK, B.; CHARNIAK, E. Noun-phrase co-occurrence statistics for
semi-automatic semantic lexicon construction. arXiv preprint
cs/0008026, 2000.
ROBERTSON, S. E.; SPÄRCK JONES, K. Relevance weighting of search terms.
Journal of the American Society for Information
science, v. 27, n. 3, p. 129–146, 1976.
ROBERTSON, S. E.; WALKER, S. Okapi/keenbow at trec-8.
TREC. Anais...Citeseer, 1999. Disponível em: <https://trec.nist.gov/pubs/trec8/papers/okapi.pdf>
ROBOTTI, C. et al. Machine Learning-based Voice Assessment for the
Detection of Positive and Recovered COVID-19 Patients. Journal
of Voice, 2021.
ROCCHIO-JR, J. J. Relevance feedback in information retrieval.
The SMART retrieval system: experiments in automatic document
processing, 1971.
RODRÍGUEZ, M. M.; BEZERRA, B. L. D. Processamento de
linguagem natural para reconhecimento de entidades nomeadas em textos
jurı́dicos de atos administrativos (portarias).
Revista de Engenharia e Pesquisa Aplicada, v. 5, n. 1,
p. 67–77, 2020.
ROGERS, A.; KOVALEVA, O.; RUMSHISKY, A. A primer in BERTology: What we
know about how BERT works. Transactions of the Association for
Computational Linguistics, v. 8, p. 842–866, 2021.
ROSA, G. M. et al. Yes, bm25
is a strong baseline for legal case retrieval. arXiv
preprint arXiv:2105.05686, b2021.
ROSA, G. M. et al. A
cost-benefit analysis of cross-lingual transfer methods.
arXiv preprint arXiv:2105.06813, a2021.
ROTH, D.; YIH, W. Global inference for entity and relation
identification via a linear programming formulation.
Introduction to statistical relational learning, p.
553–580, 2007.
ROY, K.; GARG, T.; PALIT, V. Knowledge Graph
Guided Semantic Evaluation of Language Models For User
Trust. 2023 IEEE Conference on Artificial Intelligence
(CAI). Anais...2023.
RUBIN, V. L. TALIP Perspectives, Guest Editorial Commentary: Pragmatic
and Cultural Considerations for Deception Detection in Asian Languages.
v. 13, n. 2, p. 10:1–10:8, 2014.
RUBIN, V. L.; CHEN, Y.; CONROY, N. J. Deception detection for news:
Three types of fakes. Proceedings of the Association for
Information Science and Technology, v. 52, n. 1, p. 1–4, 2015.
RUBIN, V. L.; CONROY, N. J. Challenges in automated deception detection
in computer-mediated communication. Proceedings of the American
Society for Information Science and Technology, v. 48, n. 1, p.
1–4, 2011.
RUPPENHOFER, J. et al. FrameNet II:
Extended Theory and Practice. [s.l: s.n.].
RUSSELL, M. A. Mineração de Dados da Web Social.
Primeira edição ed. São Paulo: O’Reilly Novatec, 2011.
RUSSELL-ROSE, T.; CHAMBERLAIN, J.; AZZOPARDI, L. Information retrieval
in the workplace: A comparison of professional search practices.
Information Processing & Management, v. 54, n. 6,
p. 1042–1057, 2018.
SACRAMENTO, A. DA S. B.; SOUZA, M. Joint Event Extraction with
Contextualized Word Embeddings for the Portuguese Language.
Brazilian Conference on Intelligent Systems.
Anais...Springer, 2021.
SAGER, N. Natural language information formatting: the automatic
conversion of texts to a structured data base. Em: Advances in
computers. [s.l.] Elsevier, 1978. v. 17p. 89–162.
SAGER, N.; FRIEDMAN, C.; LYMAN, M. S. Medical language
processing: computer management of narrative data. [s.l.]
Addison-Wesley Longman Publishing Co., Inc., 1987.
SAKIYAMA, K. M. Geração
Automática de Verbetações para
Recuperação de
Informações no Domı́nio
Jurı́dico Brasileiro. mathesis—[s.l.] Instituto de
Ciências Matemáticas e de Computação - Universidade de São
Paulo, 2023.
SALMINEN, J. et al. Creating and
detecting fake reviews of online products. Journal of
Retailing and Consumer Services, v. 64, p. 102771, 2022.
SALTON, G.; MCGILL, M. J. Introduction to Modern Information
Retrieval. [s.l.] McGraw-Hill, 1983.
SALVI, C. et al. Going Viral: How Fear,
Socio-Cognitive Polarization and Problem-Solving Influence Fake News
Detection and Proliferation During COVID-19 Pandemic.
Frontiers in Communication, v. 5, p. 127, 2021.
SAMY, D. Reconocimiento
y clasificación de entidades nombradas en textos legalesen
español. Procesamiento del lenguaje
natural, v. 67, p. 103–114, 2021.
SANDERSON, M. et al. Test collection based evaluation of information
retrieval systems. Foundations and Trends in
Information Retrieval, v. 4, n. 4, p. 247–375, 2010.
SANG, E. F. T. K.; DE MEULDER, F. Introduction to the
CoNLL-2003 Shared Task: Language-Independent
Named Entity Recognition. Proceedings of the Seventh Conference
on Natural Language Learning at HLT-NAACL
2003. Anais...2003. Disponível em: <https://aclanthology.org/W03-0419>
SANSONE, C.; SPERLÍ, G. Legal Information
Retrieval systems: State-of-the-art and open issues.
Information Systems, v. 106, p. 101967, 2022.
SANTANA, B. S. A computational-linguistic-based approach to
support the analysis of the discursive configuration of violence on
social media. tese de doutorado—[s.l.] Universidade Federal do
Rio Grande do Sul, 2023.
SANTOS, C. N. DOS; GUIMARÃES, V. Boosting Named Entity
Recognition with Neural Character Embeddings. (X. Duan et al.,
Eds.)Proceedings of the 5th Named Entity Workshop.
Anais...Association for Computational Linguistics,
2015.
SANTOS, D.; CARDOSO, N. Breve introdução ao HAREM. (D.
Santos, N. Cardoso, Eds.)Reconhecimento de entidades mencionadas em
português: Documentação e actas do HAREM, a primeira
avaliação conjunta na área.
Anais...Linguateca, a2007. Disponível em: <http://www.linguateca.pt/LivroHAREM/>
SANTOS, D.; CARDOSO, N. A golden resource for named
entity recognition in portuguese. Proceeding of the 7th
International conference on the computational processing of portuguese.
Anais...Springer, b2007.
SANTOS, D.; CARDOSO, N.; SECO, N. Avaliação no HAREM: Métodos e
medidas. (D. Santos, N. Cardoso, Eds.)Reconhecimento de
entidades mencionadas em português: Documentação e actas do HAREM, a
primeira avaliação conjunta na área.
Anais...Linguateca, 2007.
SANTOS, D.; ROCHA, P. The key to the first CLEF
with Portuguese: Topics, questions and answers in
CHAVE. Workshop of the Cross-Language Evaluation
Forum for European Languages. Anais...2004.
SANTOS, H. D. P. DOS et al. Fall Detection in EHR
using Word Embeddings and Deep Learning. 2019 IEEE 19th
International Conference on Bioinformatics and Bioengineering (BIBE).
Anais...a2019.
SANTOS, H. D. P. D.; ULBRICH, A. H. D. P. S.; VIEIRA, R.
Evaluation of a Prescription Outlier Detection System in
Hospital’s Pharmacy Services. 2021 IEEE International
Conference on Bioinformatics and Biomedicine (BIBM).
Anais...IEEE, a2021.
SANTOS, J. et al. Assessing the Impact of Contextual Embeddings
for Portuguese Named Entity Recognition. Proceedings of the 8th
Brazilian Conference on Intelligent Systems.
Anais...b2019.
SANTOS, J. et al. De-identification of clinical notes using
contextualized language models and a token classifier.
Brazilian Conference on Intelligent Systems.
Anais...Springer, b2021.
SANTOS, J. et al. Named entity recognition specialised for Portuguese
18th-century History research. Proceedings of the International
Conference on the Computational treatment of Portuguese,
PROPOR, a2024.
SANTOS, J.; SANTOS, H. D. P. DOS; VIEIRA, R. Fall Detection in
Clinical Notes using Language Models and Token Classifier. (A.
G. S. de Herrera et al., Eds.)Proceedings of the 33rd IEEE
International Symposium on Computer-Based Medical Systems.
Anais...a2020.
SANTOS, L. B. DOS et al. A Lightweight Regression Method to Infer
Psycholinguistic Properties for Brazilian Portuguese.
International Conference on Text, Speech, and Dialogue,
p. 281–289, 2017.
SANTOS, R. et al. Measuring the Impact of Readability Features
in Fake News Detection. (N. Calzolari et al., Eds.)Proceedings
of the Twelfth Language Resources and Evaluation Conference.
Anais...Marseille, France: European Language Resources
Association, b2020. Disponível em: <https://aclanthology.org/2020.lrec-1.176>
SANTOS, R. L. DE S. Detecção
Automática de Notícias Falsas em Português. Ph.D.
Thesis—São Carlos, Brazil: Instituto de Ciências Matemáticas e de
Computação, Universidade de São Paulo, 2022.
SANTOS, R. L. DE S.; PARDO, T. A. S. Fact-Checking for
Portuguese: Knowledge Graph and Google Search-Based Methods.
Proceedings of the 14th International Conference on the Computational
Processing of Portuguese (PROPOR). Anais...: Lecture
Notes em Artificial Intelligence (LNAI).Évora, Portugal: Springer, 2020.
SANTOS, R. L. DE S.; PARDO, T. A. S. Structural Characterization
and Graph-based Detection of Fake News in Portuguese.
Proceedings of the XIV Symposium in Information and Human Language
(STIL). Anais...2021.
SANTOS, W. R. DOS; FUNABASHI, A. M. M.; PARABONI, I.
Searching Brazilian Twitter for signs of mental health
issues. 12th International Conference on Language
Resources and Evaluation (LREC-2020).
Anais...Marseille, France: ELRA, c2020.
SANTOS, W. R. DOS; OLIVEIRA, R. L. DE; PARABONI, I. SetembroBR: a
social media corpus for depression and anxiety disorder
prediction. Language Resources and
Evaluation, v. 58, n. 1, p. 273–300, b2024.
SANTOS, W. R. DOS; PARABONI, I. Predição de transtorno
depressivo em redes sociais: BERT supervisionado ou ChatGPT
zero-shot? XIV Simpósio Brasileiro de Tecnologia
da Informação e da Linguagem Humana (STIL-2023).
Anais...Porto Alegre, Brasil: SBC, 2023.
Disponível em: <https://sol.sbc.org.br/index.php/stil/article/view/25433>
SANTOS, W. R. DOS; PARABONI, I. Prompt-based mental health
screening from social media text. Brazilian
Workshop on Social Network Analysis and Mining (BraSNAM-2024).
Anais...Brasilia, DF: 2024.
SANTOS, W. R. DOS; YOON, S.; PARABONI, I. Mental Health
Prediction from Social Media Text Using Mixture of Experts.
IEEE Latin America Transactions, v. 21, n.
6, p. 723–729, 2023.
SAQUETE, E. et al. Fighting Post-truth
using Natural Language Processing: A Review and Open Challenges.
Expert Systems with Applications, v. 141, p. 112943,
2019.
SARAH HICKEY. Nimdzi 100 - Language
Services Industry Market
Report 2020.pdf. [s.l: s.n.].
SARDINHA, T. B. Lingüística de
Corpus: histórico e problemática. DELTA: Documentação de
Estudos em Lingüística Teórica e Aplicada, v. 16, n. 2, p.
323–367, 2000.
SARMENTO, M. A.; OLIVEIRA, H. T. A. DE.
Sumarização Automática de
Artigos de Notı́cias em Português: Da
Extração à
Abstração com Abordagens
Clássicas e Modelos de Neurais.
Simpósio Brasileiro de Tecnologia da
Informação e da Linguagem Humana (STIL).
Anais...SBC, 2024.
SCAO, T. L. et al. BLOOM:
A 176B-Parameter Open-Access Multilingual Language
Model. CoRR, v. abs/2211.05100, 2022.
SCARTON, C. et al. Simplifica: a tool for authoring simplified texts in
Brazilian Portuguese guided by readability assessments.
Proceedings of the 2010 Conference of the North American Chapter
of the Association for Computational Linguistics - Human Language
Technologies, p. 41–44, 2010.
SCARTON, C. E.; ALUÍSIO, S. M. Análise
da Inteligibilidade de textos via ferramentas de
Processamento de Língua
Natural: adaptando as métricas do
Coh-Metrix para o
Português.
Linguamática, v. 2, n. 1, p. 45–61, 2010.
SCARTON, C.; SPECIA, L. Learning Simplifications for Specific Target
Audiences. Proceedings of the 56th Annual Meeting of the
Association for Computational Linguistics (Short Papers), p.
712–718, 2018.
SCHANK, R. C. et al. MARGIE: Memory Analysis Response
Generation, and Inference on English. IJCAI.
Anais...1973.
SCHMITZ, M. et al. Open language learning for information
extraction. Proceedings of the 2012 Joint Conference on
Empirical Methods in Natural Language Processing and Computational
Natural Language Learning. Anais...: EMNLP-CoNLL
’12.Stroudsburg, PA, USA: Association for Computational Linguistics;
Association for Computational Linguistics, 2012. Disponível em: <http://dl.acm.org/citation.cfm?id=2390948.2391009>
SCHNEIDER, E. T. R. et al. BioBERTpt -
A Portuguese Neural Language Model for Clinical Named
Entity Recognition. (A. Rumshisky et al., Eds.)Proceedings of
the 3rd Clinical Natural Language Processing Workshop.
Anais...Online: Association for Computational
Linguistics, nov. 2020. Disponível em: <https://aclanthology.org/2020.clinicalnlp-1.7>
SCHNEIDER, E. T. R. et al. A GPT-2 Language
Model for Biomedical Texts in Portuguese. 2021 IEEE 34th
International Symposium on Computer-Based Medical Systems (CBMS).
Anais...2021.
SCHNEIDER, E. T. R. et al. CardioBERTpt:
Transformer-based Models for Cardiology Language Representation in
Portuguese. 2023 IEEE 36th International Symposium on
Computer-Based Medical Systems (CBMS). Anais...2023.
SCHRAAGEN, M. et al. Evaluation of Named
Entity Recognition in Dutch online criminal complaints.
Computational Linguistics in the Netherlands Journal,
v. 7, p. 3–16, 2017.
SCHRIML, L. M. et al. Disease Ontology: a backbone for disease semantic
integration. Nucleic acids research, v. 40, n. D1, p.
D940–D946, 2012.
SCHUBERT, G.; FREITAS, L. A. DE. A Construção de um Corpus para
Detecção de Ironia e Sarcasmo em Português. Anais do XVII
Encontro Nacional de Inteligência Artificial e Computacional.
Anais...2020.
SCHULLER, B. W. et al. The INTERSPEECH 2021
Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19
Speech, Escalation & Primates. Interspeech 2021.
Anais...2021.
SCHUSTER, M.; PALIWAL, K. K. Bidirectional recurrent neural
networks. IEEE transactions on Signal Processing,
v. 45, n. 11, p. 2673–2681, 1997.
SEKINE, S. Description of the Japanese NE system used for
MET-2. Seventh Message Understanding Conference (MUC-7):
Proceedings of a Conference Held in Fairfax, Virginia, April 29-May 1,
1998. Anais...1998.
SELLAM, T.; DAS, D.; PARIKH, A. P. BLEURT: Learning
Robust Metrics for Text Generation. Proceedings of the 58th
Annual Meeting of the Association for Computational Linguistics,
ACL 2020, Online, July 5-10, 2020.
Anais...2020. Disponível em: <https://doi.org/10.18653/v1/2020.acl-main.704>
SELVARAJU, R. R. et al. Grad-cam: Visual explanations from deep
networks via gradient-based localization. Proceedings of the
IEEE international conference on computer vision.
Anais...2017.
SEMENOV, A. et al. Discerning Depression Propensity Among
Participants of Suicide and Depression-Related Groups of
Vk.com. Analysis of Images, Social Networks and Texts.
Anais...Cham: Springer International Publishing, 2015.
SENA, C. F. L.; CLARO, D. B. InferPortOIE: A Portuguese Open Information
Extraction system with inferences. Natural Language
Engineering, v. 25, n. 2, p. 287–306, 2019.
SENA, C. F. L.; CLARO, D. B. PragmaticOIE: a
pragmatic open information extraction for Portuguese language.
Knowl. Inf. Syst., v. 62, n. 9, p. 3811–3836, 2020.
SENA, C. F. L.; GLAUBER, R.; CLARO, D. B. Inference Approach to
Enhance a Portuguese Open Information Extraction.
Proceedings of the 19th International Conference on Enterprise
Information Systems - Volume 3: ICEIS. Anais...INSTICC;
SciTePress, 2017.
SENNRICH, R.; HADDOW, B.; BIRCH, A. Improving Neural
Machine Translation Models with Monolingual Data.
Proceedings of the 54th Annual Meeting of the Association for
Computational Linguistics (ACL 2016).
Anais...2016. Disponível em: <https://arxiv.org/abs/1511.06709>
SERRAS, F. et al. Analysing and Validating Language Complexity
Metrics Across South American Indigenous
Languages. (T. Kuribayashi et al., Eds.)Proceedings of the
Workshop on Cognitive Modeling and Computational Linguistics.
Anais...Bangkok, Thailand: Association for
Computational Linguistics, ago. 2024. Disponível em: <https://aclanthology.org/2024.cmcl-1.13/>
SHAMMA, S. A. et al. Information
Extraction from Arabic Law Documents. 2020 IEEE 14th
International Conference on Application of Information and Communication
Technologies (AICT). Anais...2020.
SHAOWEI, Z. et al. Survey of Supervised Joint Entity Relation Extraction
Methods. Journal of Frontiers of Computer Science &
Technology, v. 16, n. 4, 2022.
SHARDLOW, M. A Survey of
Automated Text Simplification. International Journal of
Advanced Computer Science and Applications(IJACSA), Special Issue on
Natural Language Processing 2014, v. 4, n. 1, 2014.
SHEIKHALISHAHI, S. et al. Natural Language Processing of Clinical Notes
on Chronic Diseases: Systematic Review. JMIR Med
Inform, v. 7, n. 2, p. e12239, abr. 2019.
SHEN, G. et al. Depression Detection via
Harvesting Social Media: A Multimodal Dictionary Learning
Solution. 26th International Joint Conference on Artificial
Intelligence, IJCAI-17. Anais...2017.
SHEN, J. H.; RUDZICZ, F. Detecting Anxiety on
Reddit. Fourth Workshop on Computational
Linguistics and Clinical Psychology: From Linguistic Signal to Clinical
Reality. Anais...Vancouver, Canada: Association for
Computational Linguistics, 2017.
SHEN, T. et al. Cross-Domain Depression
Detection via Harvesting Social Media. Twenty-Seventh
International Joint Conference on Artificial Intelligence,
IJCAI-18. Anais...International Joint
Conferences on Artificial Intelligence Organization, 2018.
SHERMIS, M. D.; BURSTEIN, J. Handbook of Automated Essay
Evaluation: Current Applications and New Directions. [s.l.]
Routledge/Taylor & Francis Group, 2013.
SHIBATA, T.; MIYAMURA, Y. LCES: Zero-shot Automated Essay
Scoring via Pairwise Comparisons Using Large Language Models.,
2025. Disponível em: <https://arxiv.org/abs/2505.08498>
SHICKEL, B. et al. Deep EHR: A Survey of Recent Advances in
Deep Learning Techniques for Electronic Health Record (EHR)
Analysis. IEEE J Biomed Health Inform, v. 22, n. 5, p.
1589–1604, out. 2017.
SHIMANAKA, H.; KAJIWARA, T.; KOMACHI, M. Machine Translation Evaluation
with BERT Regressor. arXiv, v.
abs/1907.12679, 2019.
SHRESTHA, A.; SPEZZANO, F. Detecting Depressed Users
in Online Forums. 2019 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining
(ASONAM). Anais...2019.
SHTERIONOV, D. et al. Human versus Automatic Quality Evaluation of
NMT and PBSMT. Machine
Translation, v. 32, n. 3, p. 217–235, 2018.
SHUJA, J. et al. COVID-19 open source data sets: a comprehensive survey.
Applied Intelligence, v. 51, n. 3, p. 1296–1325, 2021.
SIDDHARTHAN, A. Syntactic Simplifcation and Text Cohesion.
Research on Language and Computation - Springer, 2006.
SILVA, A. P. DA et al. Risco de queda relacionado a medicamentos em
hospitais: abordagem de aprendizado de máquina.
Acta Paulista de Enfermagem, v. 36, 2023.
SILVA, D. P. P. DA et al. Interpretability analysis of deep models for
COVID-19 detection. Artificial Intelligence in Health,
v. 1, n. 3, p. 114–126, 2024.
SILVA, F. L. V. DA et al. ABSAPT
2022 at IberLEF: Overview of the Task on Aspect-Based Sentiment Analysis
in Portuguese. Procesamiento del Lenguaje Natural,
v. 69, p. 199–205, 2022.
SILVA, F. R. A. DA. Detecção de Ironia e Sarcasmo em Língua
Portuguesa: uma abordagem utilizando Deep Learning. https://github.com/fabio-ricardo/deteccao-ironia, 2018.
SILVA, I. A. L. DA et al. Translation, post-editing and directionality.
Translation in transition: Between cognition, computing and
technology, p. 107–134, 2017.
SILVA, J. F. F. Estratégias para sumarização de
documentos. mathesis—[s.l.] (Mestrado em Engenharia
informática) - Faculdade de Ciências e Tecnologia da Universidade Nova
de Lisboa, 2022.
SILVA, M. J.; CARVALHO, P.; SARMENTO, L. Building a Sentiment
Lexicon for Social Judgement Mining. Proceedings of the 10th
International Conference on Computational Processing of the Portuguese
Language. Anais...2012.
SILVA, N. F. F. DA et al. Evaluating Topic
Models in Portuguese Political Comments About Bills from Brazil’s
Chamber of Deputies. (A. Britto, K. Valdivia Delgado,
Eds.)Intelligent Systems. Anais...Cham: Springer
International Publishing, 2021.
SILVA, N. L. DA; DI FELIPPO, A.
Descrição e Análise do
Fenômeno da Contradição para a
Sumarização Automática
Multidocumento. [s.l.] Série de
Relatórios Técnicos do Núcleo
Interinstitucional de Linguística Computacional, 2014.
SILVA, R. M. et al. Towards Automatically
Filtering Fake News in Portuguese. Expert Systems with
Applications, v. 146, p. 1–48, 2020.
SILVA, R. M. et al. Fake News Detection in Portuguese Under Large
Language Model-Generated Content. Journal of the Brazilian
Computer Society, p. 1–18, 2025.
SIQUEIRA, F. et al. Ulysses Tesemõ: a new
large corpus for Brazilian legal and governmental domain.
Language Resources and Evaluation, p. 1–20, jul. 2024.
SJÖHOLM, J. Probability as readability: A new machine learning
approach to readability assessment for written Swedish. [s.l.]
LiU Electronic Press, 2012.
SLEIMI, A. et al. An automated framework
for the extraction of semantic legal metadata from legal texts.
Empirical Software Engineering, v. 26, p. 1–50, 2021.
SMIRNOVA, A.; CUDRÉ-MAUROUX, P. Relation extraction using distant
supervision: A survey. ACM Computing Surveys (CSUR), v.
51, n. 5, p. 1–35, 2018.
SMITH, K. S. On Integrating Discourse in Machine
Translation. Proceedings of the Third Workshop on
Discourse in Machine Translation. Anais...2017.
SMYWIŃSKI-POHL, A. et al. Automatic Extraction of
Amendments from Polish Statutory Law. Proceedings of the
Eighteenth International Conference on Artificial Intelligence and Law.
Anais...: ICAIL ’21.New York, NY, USA: Association for
Computing Machinery, 2021.
SNOVER, M. G. et al. A Study of Translation Edit Rate with
Targeted Human Annotation. Proceedings of the 7th Conference of
the Association for Machine Translation in the Americas: Technical
Papers, AMTA 2006, Cambridge, Massachusetts, USA, August
8-12, 2006. Anais...2006. Disponível em: <https://aclanthology.org/2006.amta-papers.25/>
SOARES, M. O que é letramento? Presença Pedagógica Volume 2, n.
10, p. 15–25, 1996.
SOBAHI, N. et al. Explainable COVID-19 detection using fractal dimension
and vision transformer with Grad-CAM on cough sounds.
Biocybernetics and Biomedical Engineering, v. 42, n. 3,
p. 1066–1080, 2022.
SOCHER, R. et al. Semantic compositionality through recursive
matrix-vector spaces. Proceedings of the 2012 joint conference
on empirical methods in natural language processing and computational
natural language learning. Anais...2012.
SODERLAND, S. et al. CRYSTAL inducing a conceptual
dictionary. Proceedings of the 14th international joint
conference on Artificial intelligence-Volume 2.
Anais...1995.
SOLORIO, T. MALINCHE: A NER system for Portuguese that reuses knowledge
from Spanish. Reconhecimento de entidades mencionadas em
português: Documentação e atas do
HAREM, a primeira avaliação conjunta na
área, Capı́tulo, v. 10, p. 123–136,
2007.
SONG, H. et al. Feature Attention Network: Interpretable
Depression Detection from Social Media. 32nd Pacific Asia
Conference on Language, Information and Computation.
Anais...Hong Kong: Association for Computational
Linguistics, 2018.
SOUSA, A. et al. Cross-Lingual Annotation Projection for
Argument Mining in Portuguese. (G. Marreiros et al.,
Eds.)Progress in Artificial Intelligence.
Anais...Springer International Publishing, 2021.
SOUSA, M. C. P. DE. O Corpus Tycho Brahe:
contribuições para as humanidades digitais no
Brasil. Filologia e linguı́stica
portuguesa, v. 16, n. esp., p. 53–93, 2014.
SOUSA, R. F. DE; BRUM, H. B.; NUNES, M. DAS G. V. A bunch of
helpfulness and sentiment corpora in brazilian portuguese.
Proceedings of Symposium in Information and Human Language Technology.
Anais...2019.
SOUZA, E. et al. An
Information Retrieval Pipeline for Legislative Documents from the
Brazilian Chamber of Deputies. Em: Legal Knowledge and
Information Systems. [s.l.] IOS Press, 2021a. p. 119–126.
SOUZA, E. N. P. DE; CLARO, D. B.; GLAUBER, R. A Similarity Grammatical
Structures Based Method for Improving Open Information Systems.
j-jucs, v. 24, n. 1, p. 43–69, 28 jan. 2018.
SOUZA, E. N. P.; CLARO, D. B. Extração
de Relações utilizando Features Diferenciadas para Português.
Linguamática, v. 6, n. 2, p. 57–65, 2014.
SOUZA, F.; NOGUEIRA, R.; LOTUFO, R. BERTimbau: pretrained BERT
models for Brazilian Portuguese. (R. Cerri, R. C. Prati,
Eds.)Proceedings of the 2020 Brazilian Conference on Intelligent
Systems. Anais...Springer International Publishing,
a2020.
SOUZA FREIRE, P. M.; MATIAS DA SILVA, F. R.; GOLDSCHMIDT, R. R. Fake news detection
based on explicit and implicit signals of a hybrid crowd: An approach
inspired in meta-learning. Expert Systems with
Applications, v. 183, p. 115414, 2021.
SOUZA, J. W. DA C. Aprofundamento da caracterização
linguístico-computacional da complementaridade em um corpus jornalístico
multidocumento. tese de doutorado—[s.l.] (Doutorado em
Linguística) - Programa de Pós-Graduação em Linguística, Universidade
Federal de São Carlos, 2019.
SOUZA, J. W. DA C.; FELIPPO, A. D.
Caracterização da complementaridade temporal:
subsı́dios para sumarização
automática multidocumento. Alfa: Revista de
Linguı́stica (São José do Rio
Preto), v. 62, p. 125–150, 2018.
SOUZA, J. W. DA C.; FELIPPO, A. D.; PARDO, T. A. S. Investigação
da Identificação da Redundância na Sumarização Multidocumento.
Anais do III Student Workshop on Information and Human Language
Technology. Anais...a2011.
SOUZA, M. et al. Construction of a Portuguese
Opinion Lexicon from multiple resources. Proceedings of the 8th
Brazilian Symposium in Information and Human Language Technology.
Anais...b2011.
SOUZA, V.; NOBRE, J.; BECKER, K. Characterization of Anxiety,
Depression, and their Comorbidity from Texts of Social
Networks. Anais do XXXV Simpósio Brasileiro de Bancos de Dados.
Anais...Porto Alegre, Brazil: SBC, b2020.
SOUZA, V.; NOBRE, J.; BECKER, K. A Deep Learning Ensemble to Classify
Anxiety, Depression, and their Comorbidity from Texts of Social
Networks. Journal of Information and Data Management,
v. 12, n. 3, p. 306–325, b2021.
SPÄRCK JONES, K. Report on the need for and provision of an ’ideal’
information retrieval test collection. Computer
Laboratory, 1975.
SPÄRCK JONES, K.; WALKER, S.; ROBERTSON, S. E. A probabilistic model of
information retrieval: development and comparative experiments.
Information processing & management, v. 36, n. 6,
p. 809–840, 2000.
SPARCK-JONES, K. Automatic Summarizing: Factors and Directions.
In Mani, I. And Maybury, M., editors, Advances in Automatic Text
Summarization. MIT Press, 1998.
SPEER, R.; CHIN, J.; HAVASI, C. ConceptNet 5.5: an open
multilingual graph of general knowledge. Proceedings of the
Thirty-First AAAI Conference on Artificial Intelligence.
Anais...: AAAI’17.San Francisco, California, USA: AAAI
Press, 2017.
SPEICHER, T. et al. Potential for discrimination in online
targeted advertising. Proceedings of the Conference on
Fairness, Accountability and Transparency. Anais...ACM,
2018.
SRIVASTAVA, N. et al. Dropout: a simple way to prevent neural networks
from overfitting. The journal of machine learning
research, v. 15, n. 1, p. 1929–1958, 2014.
SRIVASTAVA, S.; SHARMA, G. OmniVec2 - A Novel
Transformer Based Network for Large Scale Multimodal and Multitask
Learning. 2024 IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR). Anais...2024.
STANOJEVIC, M.; SIMA’AN, K. BEER: BEtter Evaluation
as Ranking. Proceedings of the Ninth Workshop on Statistical
Machine Translation, WMT@ACL 2014, June 26-27, 2014, Baltimore,
Maryland, USA. Anais...2014. Disponível
em: <https://doi.org/10.3115/v1/w14-3354>
STANOVSKY, G. et al. Supervised open information
extraction. Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational Linguistics: Human
Language Technologies, Volume 1 (Long Papers).
Anais...2018.
STJ. Supremo Tribunal de Justiça — Composição. https://www.stj.jus.br/sites/portalp/Institucional/Composicao,
maio 24DC.
STRIEN, D. VAN et al. Assessing the impact of OCR quality on
downstream NLP tasks. ICAART 2020 - Proceedings of the 12th
International Conference on Agents and Artificial Intelligence.
Anais...2020.
SU, J.; CARDIE, C.; NAKOV, P. Adapting Fake
News Detection to the Era of Large Language Models.
Proceedings of the Findings of the Association for Computational
Linguistics: NAACL 2024. Anais...2024.
SU, K.-Y.; WU, M.-W.; CHANG, J.-S. A new quantitative quality
measure for machine translation systems. Proceedings of the
14th conference on Computational linguistics -.
Anais...Association for Computational Linguistics,
1992. Disponível em: <http://dx.doi.org/10.3115/992133.992137>
SUN, C.; EMONET, V.; DUMONTIER, M. A Comprehensive Comparison of
Automated FAIRness Evaluation Tools. (K. Wolstencroft et al.,
Eds.)13th International Conference on Semantic Web Applications and
Tools for Health Care and Life Sciences, SWAT4HCLS 2022,
Virtual Event, Leiden, The Netherlands, January 10th to 14th, 2022.
Anais...: CEUR Workshop
Proceedings.CEUR-WS.org, 2022. Disponível em: <http://ceur-ws.org/Vol-3127/paper-6.pdf>
SUTSKEVER, I.; VINYALS, O.; LE, Q. V. Sequence to Sequence
Learning with Neural Networks. (Z. Ghahramani et al.,
Eds.)Advances in Neural Information Processing Systems 27: Annual
Conference on Neural Information Processing Systems 2014, December 8-13
2014, Montreal, Quebec, Canada. Anais...2014.
Disponível em: <https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html>
SWEET, P. L. Every bone of my body: Domestic violence and the diagnostic
body. Social Science & Medicine, v. 122, p. 44–52,
2014.
TAKAMATSU, S.; SATO, I.; NAKAGAWA, H. Reducing wrong labels in
distant supervision for relation extraction. Proceedings of the
50th Annual Meeting of the Association for Computational Linguistics
(Volume 1: Long Papers). Anais...2012.
TAN, K. L.; LEE, C. P.; LIM, K. M. A Survey of Sentiment Analysis:
Approaches, Datasets, and Future Research. Applied
Sciences, 2023.
TANDOC JR., E. C.; LIM, Z. W.; LING, R. Defining
“Fake News”. Digital Journalism, v. 6,
n. 2, p. 137–153, 2018.
TAUS. TAUS - The
Translation Industry in 2022
Report., 2020. Disponível em: <https://info.taus.net/translation-industry-2022-report-download>.
Acesso em: 19 ago. 2020
TEAM, G. et al. Gemma: Open Models Based on Gemini Research and
Technology., 2024. Disponível em: <https://arxiv.org/abs/2403.08295>
TEIXEIRA, E. N.; FONSECA, M. C. M.; SOARES, M. E. Resolução do pronome
nulo em Português Brasileiro: Evidência de movimentação ocular.
VEREDAS: Sintaxe das Línguas Brasileiras, v. 18, 2014.
TEIXEIRA, S. C. S. B.; MARENGO, S. M. D. A.; FINATTO, M. J. B. Construindo
fichas terminológicas para estudos sócio-históricos. Revista
Diálogos, v. 10, n. 3, p. 261–279, 2022.
TEIXEIRA, S. H.; ZAMORA, M. H. Pensando a interseccionalidade a partir
da vida e morte de Marielle Franco.
Dignidade Re-Vista, 2019.
THAKAR, H.; BHATT, B. Fake News Detection:
Recent Trends and Challenges. Social Network Analysis and
Mining, v. 14, 2024.
THOMAS, C. et al. Automatic Detection and Rating of Dementia of
Alzheimer Type through Lexical Analysis of Spontaneous Speech.
Proceedings of the IEEE International Conference on Mechatronics
and Automation, p. 1569–1574, 2005.
THORNE, J.; VLACHOS, A. Automated Fact Checking: Task
Formulations, Methods and Future Directions. (E. M. Bender, L.
Derczynski, P. Isabelle, Eds.)Proceedings of the 27th International
Conference on Computational Linguistics. Anais...Santa
Fe, New Mexico, USA: Association for Computational Linguistics, ago.
2018.
TIRRELL, L. Toxic Speech: Inoculations and Antidotes. The
Southern Journal of Philosophy, 2018.
TOLLES, J.; MEURER, W. J. Logistic Regression:
Relating Patient Characteristics to Outcomes. JAMA,
v. 316, n. 5, p. 533–534, ago. 2016.
TORAL, A. et al. Attaining the Unattainable? Reassessing
Claims of Human Parity in Neural Machine Translation.
Proceedings of WMT. Anais...Brussels,
Belgium: 2018.
TORRENT, T. T. et al. Representing Context in
FrameNet: A Multidimensional, Multimodal Approach. Frontiers
in Psychology, v. Volume 13 - 2022, 2022.
TORRENT, T. T. et al. A flexible tool for a
qualia-enriched FrameNet: the
FrameNet Brasil
WebTool. Language Resources and
Evaluation, jan. 2024.
TRAJANO, D.; BORDINI, R. H.; VIEIRA, R. OLID-BR: offensive language
identification dataset for Brazilian Portuguese. Language
Resources and Evaluation, 2023.
TRIFU, R. et al. Linguistic
indicators of language in major depressive disorder (MDD). An evidence
based research. Journal of Evidence-Based
Psychotherapies, v. 17, p. 105–128, mar. 2017.
TROTZEK, M.; KOITKA, S.; FRIEDRICH, C. M. Utilizing neural networks and
linguistic metadata for early detection of depression indications in
text sequences. IEEE Transactions on Knowledge and Data
Engineering, 2018.
TSUGAWA, S. et al. Recognizing Depression from Twitter
Activity. 33rd Annual ACM Conference on Human Factors in
Computing Systems. Anais...New York, USA:
Association for Computing Machinery, 2015.
TURCHIOE, M. R. et al. Systematic review of
current natural language processing methods and applications in
cardiology. Heart, v. 108, n. 12, p. 909–916, 2022.
UCHIDA, H.; ZHU, M.; DELLA SENTA, T. A gift for a millennium.
IAS/UNU, Tokyo, 1999.
USZKOREIT, H.; LOMMEL, A. Multidimensional
Quality Metrics: A
New Unified Paradigm for
Human and Machine Translation
Quality Assessment. [s.l: s.n.].
VAJJALA, S.; MEURERS, D. Readability-based Sentence Ranking for
Evaluating Text Simplification. CoRR, v.
abs/1603.06009, 2016.
VAN DER LAAN, R. H. Tesauro e terminologia:
uma inter-relação lógica. Tese (Doutorado em Letras)—Porto
Alegre: Universidade Federal do Rio Grande do Sul; Instituto de Letras,
Programa de Pós-Graduação em Letras, 2002.
VARGAS, D. F.; VAN DER LANN, R. H. A
contribuição da terminologia na
construção de linguagens
documentárias como os tesauros. Biblos, v.
25, n. 1, p. 21–34, 2011.
VARGAS, F. et al. HateBR: A Large
Expert Annotated Corpus of Brazilian Instagram
Comments for Offensive Language and Hate Speech Detection.
Proceedings of the Thirteenth Language Resources and Evaluation
Conference. Anais...a2022.
VARGAS, F. et al. Rhetorical Structure Approach for Online
Deception Detection: A Survey. (N. Calzolari et al.,
Eds.)Proceedings of the Thirteenth Language Resources and Evaluation
Conference. Anais...Marseille, France: European
Language Resources Association, jun. b2022. Disponível em: <https://aclanthology.org/2022.lrec-1.635>
VARGAS, F. et al. Improving Explainable Fact-Checking via
Sentence-Level Factual Reasoning. (M. Schlichtkrull et al.,
Eds.)Proceedings of the Seventh Fact Extraction and VERification
Workshop (FEVER). Anais...Miami, Florida, USA:
Association for Computational Linguistics, nov. 2024. Disponível em:
<https://aclanthology.org/2024.fever-1.23/>
VARGAS, F. A.; SANTOS, R. S. S. D.; ROCHA, P. R. Identifying
Fine-Grained Opinion and Classifying Polarity on Coronavirus
Pandemic. Proceedings of the Brazilian Conference on
Intelligent Systems. Anais...2020.
VARGAS, F.; PARDO, T.; BENEVENUTO, F. Socially Responsible and
Explainable Automated Fact-Checking and Hate Speech Detection.
Anais do XXXVIII Concurso de Teses e Dissertações.
Anais...Porto Alegre, RS, Brasil: SBC, 2025. Disponível
em: <https://sol.sbc.org.br/index.php/ctd/article/view/36355>
VAROL, O. et al. Online human-bot interactions: Detection,
estimation, and characterization. Proceedings of the
International AAAI Conference on Web and Social Media.
Anais...AAAI Press, 2017.
VASWANI, A. et al. Attention is All you Need. (I. Guyon
et al., Eds.)Advances in Neural Information Processing Systems.
Anais...Curran Associates, Inc., 2017. Disponível em:
<https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html>
VIEIRA, C.; SOUZA, J.; CAVALCANTI, G. Detecção de Fake News em
Português: Análise Comparativa entre Métodos de Representação em
Português, Inglês e Multilíngues. Anais do XIV Brazilian
Workshop on Social Network Analysis and Mining.
Anais...Porto Alegre, RS, Brasil: SBC, 2025. Disponível
em: <https://sol.sbc.org.br/index.php/brasnam/article/view/36378>
VIEIRA, J. M. M. The Brazilian
Portuguese eye tracking corpus with a predictability study focusing on
lexical and partial prediction. mathesis—Universidade
Federal do Ceará, Biblioteca Universitária: Federal University of Ceará
(UFC), 2020.
VIEIRA, R. et al. Enriching the 1758 Portuguese Parish Memories
(Alentejo) with Named Entities. Journal of Open Humanities
Data, v. 7, p. 20, 2021.
VILAR, D. et al. Error Analysis of Statistical Machine
Translation Output. Proceedings of the Fifth International
Conference on Language Resources and Evaluation
(LREC’06). Anais...Genoa,
Italy: European Language Resources Association (ELRA), 2006. Disponível
em: <http://www.lrec-conf.org/proceedings/lrec2006/pdf/413_pdf.pdf>
VILLAR, G. S.; FINATTO, M. J. B. Acessibilidade textual e
terminológica: novos glossários sobre
oncologia para a ferramenta MedSimples. Mandinga-Revista de
Estudos Linguı́sticos (ISSN: 2526-3455), v. 7, n.
2, p. 23–42, 2023.
VIMAL, B. et al. MFCC Based Audio
Classification Using Machine Learning. 2021 12th
International Conference on Computing Communication and Networking
Technologies (ICCCNT). Anais...2021.
VIRIDIANO, M. et al. Framed Multi30K:
A Frame-Based Multimodal-Multilingual Dataset. (N. Calzolari et
al., Eds.)Proceedings of the 2024 Joint International Conference on
Computational Linguistics, Language Resources and Evaluation
(LREC-COLING 2024). Anais...Torino, Italia: ELRA; ICCL,
2024. Disponível em: <https://aclanthology.org/2024.lrec-main.656/>
VITÓRIO, D. et al. Ulysses-RFSQ: A Novel
Method to Improve Legal Information Retrieval Based on Relevance
Feedback. (J. C. Xavier-Junior, R. A. Rios,
Eds.)Intelligent Systems. Anais...Cham: Springer
International Publishing, 2022.
VITÓRIO, D. et al. Building a relevance
feedback corpus for legal information retrieval in the real-case
scenario of the Brazilian Chamber of
Deputies. Language Resources and
Evaluation, 2024.
VLACHOS, A.; RIEDEL, S. Fact checking: Task definition and
dataset construction. Proceedings of the ACL 2014 Workshop on
Language Technologies and Computational Social Science.
Anais...Association for Computational Linguistics,
2014.
VOSOUGHI, S.; ROY, D.; ARAL, S. The spread of true and
false news online. Science, v. 359, n. 6380, p.
1146–1151, 2018.
WAGNER FILHO, J. A. et al. The brWaC
Corpus: A New Open Resource for Brazilian
Portuguese. Proceedings of the Eleventh
International Conference on Language Resources and Evaluation
(LREC 2018). Anais...Miyazaki, Japan:
European Language Resources Association (ELRA), 2018. Disponível em:
<https://aclanthology.org/L18-1686>
WANDERLEY, M. G. et al. A Moving Target: Detecting Concept Drift
in Brazilian Portuguese Fake News. Proceedings of the 16th
Symposium in Information and Human Language Technology (STIL’2025).
Anais...Fortaleza, CE, Brazil: SBC, out. 2025.
WANG, L. et al. Relation classification via multi-level
attention cnns. Proceedings of the 54th Annual Meeting of the
Association for Computational Linguistics (Volume 1: Long Papers).
Anais...2016.
WANG, S. et al. Want To
Reduce Labeling Cost? GPT-3 Can Help. (M.-F.
Moens et al., Eds.)Findings of the Association for Computational
Linguistics: EMNLP 2021. Anais...Punta Cana, Dominican
Republic: Association for Computational Linguistics, nov. 2021.
WANG, S. et al. GPT-NER: Named Entity Recognition via Large
Language Models., 2023. Disponível em: <https://arxiv.org/abs/2304.10428>
WANG, W. Y. “Liar, Liar
Pants on Fire”: A New Benchmark Dataset for Fake News
Detection. Proceedings of the 55th Annual Meeting of the
Association for Computational Linguistics (Volume 2: Short Papers).
Anais...Vancouver, Canada: Association for
Computational Linguistics, jul. 2017.
WARDLE, C.; DERAKHSHAN, H. Information Disorder: Toward an
Interdisciplinary Framework for Research and Policy Making.
[s.l.] Council of Europe, 2017. Disponível em: <https://rm.coe.int/information-disorder-toward-an-interdisciplinary-framework-for-researc/168076277c>.
WASSERMAN, S.; FAUST, K. Social network analysis: Methods and
applications. [s.l.] Cambridge university press, 1994.
WATANABE, W. M. et al. Facilita: helping the reading of texts
available on the web. XV Brazilian Symposium on
Multimedia and the Web, WebMedia ’09, Fortaleza, Ceará,
Brazil, October 5-7, 2009. Anais...a2009. Disponível
em: <http://doi.acm.org/10.1145/1858477.1858516>
WATANABE, W. M. et al. Facilita: reading assistance for
low-literacy readers. Proceedings of the 27th Annual
International Conference on Design of Communication, SIGDOC
2009, Bloomington, Indiana, USA, October 5-7, 2009.
Anais...b2009. Disponível em: <http://doi.acm.org/10.1145/1621995.1622002>
WATASE, T. et al. Severity Classification Using Dynamic Time
Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility
Cross-Sectional Study. JMIR Biomedical Engineering, v.
8, n. 1, p. e50924, 2023.
WAY, A. Quality
Expectations of Machine Translation. Em: MOORKENS, J. et al. (Eds.).
Translation Quality Assessment: From Principles to
Practice. Cham: Springer International Publishing, 2018. p.
159–178.
WAY, A.; FORCADA, M. L. Editors’ foreword to
the invited issue on SMT and NMT.
Machine Translation, v. 32, n. 3, p. 191–194, set.
2018.
WEI, X. et al. ChatIE: Zero-Shot Information Extraction via
Chatting with ChatGPT., 2024. Disponível em: <https://arxiv.org/abs/2302.10205>
WHO. Comprehensive mental health action plan 2013–2030.
[s.l.] World Health Organization; World Health Organization, 2021.
WHO. World Health Organization.
World Health Organization, 2024. Disponível em: <https://www.who.int/health-topics/violence-against-women#tab=tab_2>.
Acesso em: 6 abr. 2024
WILKINSON, M.; DUMONTIER, M.; AALBERSBERG, ET AL. The FAIR Guiding
Principles for scientific data management and stewardship.
Scientific data, v. 3, n. 1, p. 1–9, 2016.
WILKINSON, M.; DUMONTIER, M.; SANSONE, ET AL. Evaluating
FAIR maturity through a scalable, automated, community-governed
framework. Sc. Data, v. 6, n. 1, p. 1–12,
2019.
WIVES, L. K. Técnicas de
Recuperação de
Informações Com Ênfase em
Informações Textuais. tese de
doutorado—[s.l.] Universidade Federal do Rio Grande do Sul, 1997.
WOLFRAM, W. Variation and
Language: Overview. Em: BROWN, K. (Ed.). Encyclopedia of
Language & Linguistics (Second Edition). Second Edition ed.
Oxford: Elsevier, 2006. p. 333–341.
WOLINSKI, F.; VICHOT, F.; DILLET, B. Automatic processing proper
names in texts. Proc. Conference on European Chapter of the
Association for Computational Linguistics.
Anais...EACL, 1995.
WU, H. et al. SemEHR: A general-purpose semantic search
system to surface semantic data from clinical notes for tailored care,
trial recruitment, and clinical research. J Am Med Inform
Assoc, v. 25, n. 5, p. 530–537, 2018.
WU, Y. et al. Google’s neural machine translation system: Bridging the
gap between human and machine translation. arXiv preprint
arXiv:1609.08144, 2016.
XAVIER, C. C.; LIMA, V. L. S. DE; SOUZA, M. Open information extraction
based on lexical semantics. Journal of the Brazilian Computer
Society, v. 21, n. 1, p. 1–14, 2015.
XAVIER, R. C. Português no Direito: Linguagem Forense.
Rio de Janeiro: Forense, 2002. p. 1
XIA, P. et al. LOME: Large Ontology Multilingual
Extraction. (D. Gkatzia, D. Seddah, Eds.)Proceedings of the
16th Conference of the European Chapter of the Association for
Computational Linguistics: System Demonstrations.
Anais...Online: Association for Computational
Linguistics, abr. 2021. Disponível em: <https://aclanthology.org/2021.eacl-demos.19>
XIA, T.; HAN, J.; MASCOLO, C. Exploring machine learning for audio-based
respiratory condition screening: A concise review of databases, methods,
and open issues. Experimental Biology and Medicine, v.
247, n. 22, p. 2053–2061, 2022.
XIAO, C. et al. Human-AI Collaborative
Essay Scoring: A Dual-Process Framework with LLMs.
Proceedings of the 15th International Learning Analytics and Knowledge
Conference. Anais...: LAK ’25.New York, NY, USA:
Association for Computing Machinery, 2025.
XU, S. et al. Automated Lightweight
Model for Asthma Detection Using Respiratory and Cough Sound
Signals. Diagnostics, v. 15, n. 9, 2025.
XU, W.; CALLISON-BURCH, C.; NAPOLES, C. Problems in Current Text
Simplification Research: New Data Can Help. Transactions of
the Association for Computational Linguistics, v. 3, p.
283–297, 2015.
YADAV, S. et al. Identifying
Depressive Symptoms from Tweets: Figurative Language Enabled Multitask
Learning Framework. 28th International Conference on
Computational Linguistics. Anais...Barcelona, Spain
(Online): International Committee on Computational Linguistics, 2020.
YAN, M. Y.; GUSTAD, L. T.; NYTRØ, Ø. Sepsis prediction, early detection,
and identification using clinical text for machine learning: a
systematic review. J Am Med Inform Assoc, v. 29, n. 3,
p. 559–575, jan. 2022.
YANG, H. et al. Clinical Trial Classification of SNS24 Calls with Neural
Networks. Future Internet, v. 14, n. 5, p. 130, 2022.
YANG, K.-C. et al. Scalable and generalizable social bot detection
through data selection. Proceedings of the AAAI Conference on
Artificial Intelligence, v. 34, n. 01, p. 1096–1103, a2020.
YANG, P.; FANG, H.; LIN, J. Anserini: Enabling the use of lucene
for information retrieval research. Proceedings of the 40th
international ACM SIGIR conference on research and development in
information retrieval. Anais...2017.
YANG, R. et al. Enhancing automated essay scoring performance
via fine-tuning pre-trained language models with combination of
regression and ranking. Association for Computational
Linguistics (ACL), b2020.
YASUNAGA, M. et al. Deep Bidirectional Language-Knowledge Graph
Pretraining., 2022. Disponível em: <https://arxiv.org/abs/2210.09338>
YATES, A.; COHAN, A.; GOHARIAN, N. Depression and Self-Harm
Risk Assessment in Online Forums. Conference on Empirical
Methods in Natural Language Processing.
Anais...Copenhagen, Denmark: Association for
Computational Linguistics, 2017.
YAZDAVAR, A. H. et al. Semi-Supervised Approach
to Monitoring Clinical Depressive Symptoms in Social Media.
IEEE/ACM International Conference on Advances in Social Network
Analysis and Mining. Anais...2017.
YU, X.; LAM, W. Jointly identifying entities and extracting
relations in encyclopedia text via a graphical model approach.
Coling 2010: Posters. Anais...2010.
YUAN, W.; NEUBIG, G.; LIU, P. BARTScore: Evaluating Generated
Text as Text Generation. Advances in Neural Information
Processing Systems 34: Annual Conference on Neural Information
Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual.
Anais...a2021. Disponível em: <https://proceedings.neurips.cc/paper/2021/hash/e4d2b6e6fdeca3e60e0f1a62fee3d9dd-Abstract.html>
YUAN, Y. et al. A relation-specific attention network for joint
entity and relation extraction. International joint conference
on artificial intelligence. Anais...International Joint
Conference on Artificial Intelligence, b2021.
YUE, Z. et al. Evidence-Driven Retrieval Augmented Response
Generation for Online Misinformation. (K. Duh, H. Gomez, S.
Bethard, Eds.)Proceedings of the 2024 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies (Volume 1: Long Papers). Anais...Mexico
City, Mexico: Association for Computational Linguistics, jun. 2024.
Disponível em: <https://aclanthology.org/2024.naacl-long.313>
ZANUZ, L.; RIGO, S. J. Fostering Judiciary
Applications with New Fine-Tuned Models for Legal Named Entity
Recognition in Portuguese. (V. Pinheiro et al.,
Eds.)Computational Processing of the Portuguese Language.
Anais...Cham: Springer International Publishing, 2022.
ZAROCOSTAS, J. How to fight an infodemic. The lancet,
v. 395, n. 10225, p. 676, 2020.
ZELENINA, M. Eye Tracking for NLP.
SlideShare, 2015. Disponível em: <https://www.slideshare.net/mariezelenina/presentation-2-47610828>
ZELENKO, D.; AONE, C.; RICHARDELLA, A. Kernel methods for relation
extraction. Journal of machine learning research, v. 3,
n. Feb, p. 1083–1106, 2003.
ZENG, D. et al. Relation classification via convolutional deep
neural network. Proceedings of COLING 2014, the 25th
international conference on computational linguistics: technical papers.
Anais...2014.
ZHANG, A. et al. Dive into Deep Learning. [s.l.]
Cambridge University Press, 2023.
ZHANG, H. The Optimality of Naive Bayes. Proceedings of
the Seventeenth International Florida Artificial Intelligence Research
Society Conference. Anais...2004.
ZHANG, S. et al. Opt: Open
pre-trained transformer language models. arXiv preprint
arXiv:2205.01068, 2022.
ZHANG, S. X. et al. Predictors of
Depression and Anxiety Symptoms in Brazil during COVID-19.
Int J Environ Res Public Health, v. 18, n. 13, 30 jun.
2021.
ZHANG, S.; DUH, K.; VAN DURME, B. Mt/ie: Cross-lingual open
information extraction with neural sequence-to-sequence models.
Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 2, Short Papers.
Anais...2017.
ZHANG, T. et al. BERTScore: Evaluating Text Generation with
BERT. 8th International Conference on Learning
Representations, ICLR 2020, Addis Ababa, Ethiopia, April
26-30, 2020. Anais...OpenReview.net, 2020. Disponível
em: <https://openreview.net/forum?id=SkeHuCVFDr>
ZHANG, Y. et al. A Comprehensive Survey on Process-Oriented
Automatic Text Summarization with Exploration of LLM-Based
Methods., 2025. Disponível em: <https://arxiv.org/abs/2403.02901>
ZHAO, S.; GRISHMAN, R. Extracting relations with integrated
information using kernel methods. Proceedings of the 43rd
annual meeting of the association for computational linguistics
(acl’05). Anais...2005.
ZHONG, H. et al. How Does
NLP Benefit Legal System: A Summary of Legal Artificial
Intelligence. Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics.
Anais...Association for Computational Linguistics,
2020.
ZHOU, L. et al. A comparison of classification methods for predicting
deception in computer-mediated communication. Journal of
Management Information Systems, v. 20, n. 4, p. 139–165, 2004.
ZHOU, L. An empirical investigation of deception behavior in instant
messaging. IEEE Transactions on Professional
Communication, v. 48, n. 2, p. 147–160, 2005.
ZHUANG, F. et al. A
comprehensive survey on transfer learning. Proceedings of
the IEEE, v. 109, n. 1, p. 43–76, 2020.
ZILIO, L.; FINATTO, M. J.; VIEIRA, R. Named Entity Recognition
Applied to Portuguese Texts from the XVIII
Century. (C. Trojahn et al., Eds.)Proceedings of the Second
Workshop on Digital Humanities and Natural Language Processing (2nd
DHandNLP 2022) co-located with International Conference on the
Computational Processing of Portuguese (PROPOR 2022),
Virtual Event, Fortaleza, Brazil, 21st March, 2022.
Anais...: CEUR Workshop
Proceedings.CEUR-WS.org, 2022. Disponível em: <http://ceur-ws.org/Vol-3128/paper10.pdf>
ZILIO, L.; LAZZARI, R. R.; FINATTO, M. J. B. NLP for historical
Portuguese: Analysing 18th-century medical texts. Proceedings of
the International Conference on the Computational treatment of
Portuguese, PROPOR, 2024.
ZOBEL, J. How reliable are the results of large-scale
information retrieval experiments? Proceedings of the 21st
annual international ACM SIGIR conference on Research and development in
information retrieval. Anais...ACM, 1998.