Referências

ABADJI, J. et al. Towards a Cleaner Document-Oriented Multilingual Crawled Corpus. Proceedings of the Thirteenth Language Resources and Evaluation Conference. Anais...Marseille, France: European Language Resources Association, jun. 2022. Disponível em: <https://aclanthology.org/2022.lrec-1.463>
ABNEY, S. P. Parsing By Chunks. Em: BERWICK, R. C.; ABNEY, S. P.; TENNY, C. (Eds.). Principle-Based Parsing: Computation and Psycholinguistics. Dordrecht: Springer Netherlands, 1992. p. 257–278.
ABREU, S. C. DE; VIEIRA, R. Relp: Portuguese open relation extraction. KO KNOWLEDGE ORGANIZATION, v. 44, n. 3, p. 163–177, 2017.
AFANTENOS, S.; ASHER, N. Counter-argumentation and discourse: A case study. Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing. Anais...CEUR Workshop Proceedings, 2014.
AFONSO, S. et al. Floresta Sintá(c)tica: A treebank for Portuguese. Proceedings of the Third International Conference on Language Resources and Evaluation (LREC02). Anais...Las Palmas, Canary Islands - Spain: European Language Resources Association (ELRA), maio 2002. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2002/pdf/1.pdf>
AGHAJANYAN, A.; GUPTA, S.; ZETTLEMOYER, L. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning. (C. Zong et al., Eds.)Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.acl-long.568>
AGICHTEIN, E.; GRAVANO, L. Snowball: Extracting relations from large plain-text collections. Proceedings of the fifth ACM conference on Digital libraries. Anais...2000.
AGIRRE, E. Cross-Lingual Word Embeddings. Computational Linguistics, v. 46, n. 1, p. 245–248, mar. 2020.
AHN, L. VON; KEDIA, M.; BLUM, M. Verbosity: A Game for Collecting Common-Sense Facts. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Anais...: CHI ’06.New York, NY, USA: Association for Computing Machinery, 2006. Disponível em: <https://doi.org/10.1145/1124772.1124784>
AI and Ethics. Springer, 2023. Disponível em: <https://link.springer.com/journal/43681/volumes-and-issues>. Acesso em: 7 abr. 2023
AJAY, H. B.; TILLET, P.; PAGE, E. B. Analysis of essays by computer (AEC-II). Storrs, CT: Univeristy of Connecticut, 1973.
AKÇAY, M. B.; OĞUZ, K. Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Communication, v. 116, p. 56–76, 2020.
ALAM, T.; KHAN, A.; ALAM, F. Punctuation Restoration using Transformer Models for High-and Low-Resource Languages. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). Anais...Online: Association for Computational Linguistics, nov. 2020. Disponível em: <https://aclanthology.org/2020.wnut-1.18>
ALCAIM, A.; SOLEWICZ, J. A.; MORAES, J. A. DE. Freqüência de ocorrência dos fones e listas de frases foneticamente balanceadas no português falado no Rio de Janeiro. Journal of Communication and Information Systems, v. 7, n. 1, 1992.
ALEIXO, P.; PARDO, T. A. S. CSTTool: um parser multidocumento automático para o Português do Brasil. IV Workshop on MSc Dissertation and PhD Thesis in Artificial Intelligence–WTDIA. Anais...b2008.
ALEIXO, P.; PARDO, T. A. S. CSTNews: um córpus de textos jornalísticos anotados segundo a teoria discursiva multidocumento CST (Cross-document Structure Theory. [s.l.] Universidade de São Paulo (USP); São Carlos, SP, Brasil., a2008. Disponível em: <http://repositorio.icmc.usp.br//handle/RIICMC/6761>.
ALENCAR, L. F. DE. Donatus: uma interface amigável para o estudo da sintaxe formal utilizando a biblioteca em Python do NLTK. Alfa: Revista de Linguística (São José do Rio Preto), v. 56, n. 2, p. 523–555, jul. 2012.
ALENCAR, L. F. DE; CUCONATO, B.; RADEMAKER, A. MorphoBr: an open source large-coverage full-form lexicon for morphological analysis of Portuguese. Texto Livre, v. 11, n. 3, p. 1–25, dez. 2018.
ALENCAR, V.; ALCAIM, A. LSF and LPC-derived features for large vocabulary distributed continuous speech recognition in Brazilian Portuguese. 2008 42nd Asilomar Conference on Signals, Systems and Computers. Anais...IEEE, 2008.
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.
ALISSON, S. Their god is not our god. Disponível em: <https://www.thecontinent.org/_files/ugd/287178_73f3d2af22614e678f277b631a62e491.pdf>. Acesso em: 11 jun. 2023.
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>
ALTUNYURT, L.; ORHAN, Z.; GÜNGÖR, T. A Composite Approach for Part of Speech Tagging in Turkish. 2006. Disponível em: <https://api.semanticscholar.org/CorpusID:9439761>
ALUÍSIO, S. et al. An Account of the Challenge of Tagging a Reference Corpus for Brazilian Portuguese. (N. J. Mamede et al., Eds.)Computational Processing of the Portuguese Language. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
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. O. F. DO. O reconhecimento de entidades nomeadas por meio de conditional random fields para a lı́ngua portuguesa. Dissertação de Mestrado, Pontifı́cia Universidade Católica do Rio Grande do Sul, 2013.
AMARAL, D.; VIEIRA, R. Nerp-crf: uma ferramenta para o reconhecimento de entidades nomeadas por meio de conditional random fields. Linguamática (Braga), 2014.
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.
ANACLETO, J. et al. Can Common Sense uncover cultural differences in computer applications? (M. Bramer, Ed.)Artificial Intelligence in Theory and Practice. Anais...Boston, MA: Springer US, 2006.
ANACLETO, J. C. et al. A Common Sense-Based On-Line Assistant for Training Employees. (C. Baranauskas et al., Eds.)Human-Computer Interaction – INTERACT 2007. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2007.
ANANIADOU, S.; MCNAUGHT, J. Text Mining for Biology And Biomedicine. Norwood, MA, USA: Artech House, Inc., 2005.
ANANTHAKRISHNAN, S.; NARAYANAN, S. S. Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence. IEEE Transactions on Audio, Speech, and Language Processing, v. 16, n. 1, p. 216–228, 2008.
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.
ANTUNES, I. Lutar com palavras: coesão e coerência. [s.l.] Parábola, 2007.
ANTUNES, I. Textualidade: noções básicas e implicações pedagógicas. [s.l.] Editora: Parábola Editorial, 2017.
ARAUJO, P. H. L. DE et al. LeNER-Br: A Dataset for Named Entity Recognition in Brazilian Legal Text. Proceedings of the 13th International Conference. Anais...2018.
ARDILA, R. et al. Common voice: A massively-multilingual speech corpus. arXiv preprint arXiv:1912.06670, 2019.
Artificial intelligence and human rights. 1. ed. [s.l.] Dykinson, S.L., 2021.
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.
ASHER, N. et al. Discourse structure and dialogue acts in multiparty dialogue: the STAC corpus. 10th International Conference on Language Resources and Evaluation (LREC 2016). Anais...2016.
ASHER, N.; LASCARIDES, A. Logics of conversation. [s.l.] Cambridge University Press, 2003.
ASHER, N.; VIEU, L. Subordinating and coordinating discourse relations. Lingua, v. 115, n. 4, p. 591–610, 2005.
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.
AUER, S. et al. DBpedia: A Nucleus for a Web of Open Data. (K. Aberer et al., Eds.)The Semantic Web. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2007.
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.
AZIZ, W.; SPECIA, L. Fully Automatic Compilation of a Portuguese-English Parallel Corpus for Statistical Machine Translation. STIL 2011. Anais...Cuiabá, MT: 2011.
BAADER, F. et al. The Description Logic Handbook: Theory, Implementation and Applications. Cambridge, Reino Unido: Cambridge University Press, 2003.
BÄCKSTRÖM, T. et al. Introduction to Speech Processing. 2. ed. [s.l: s.n.].
BADENE, S. et al. Learning Multi-party Discourse Structure Using Weak Supervision. 25th International conference on computational linguistics and intellectual technologies (Dialogue 2019). Anais...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.
BAGGA, A.; BALDWIN, B. Algorithms for Scoring Coreference Chains. Proceedings of the first International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference. Anais...Granada, Spain: 1998.
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>
BAKER, C. F.; FILLMORE, C. J.; LOWE, J. B. The Berkeley FrameNet Project. 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1. Anais...Montreal, Quebec, Canada: Association for Computational Linguistics, ago. 1998. Disponível em: <https://aclanthology.org/P98-1013>
BAKER, C.; FELLBAUM, C.; PASSONNEAU, R. Semantic Annotation of MASC. Em: Handbook of Linguistic Annotation. [s.l.] Springer Netherlands, 2017. p. 699–717.
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.
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>
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>
BARREIRA, R.; PINHEIRO, V.; FURTADO, V. FrameFOR Uma Base de Conhecimento de Frames Semânticos para Perı́cias de Informática (FrameFOR - a Knowledge Base of Semantic Frames for Digital Forensics)[In Portuguese]. Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology. Anais...Uberlândia, Brazil: Sociedade Brasileira de Computação, out. 2017. Disponível em: <https://aclanthology.org/W17-6620>
BARROS, D. L. P. DE. Introdução à Linguística II: princípios de análise. Em: FIORIN, J. L. (Ed.). 5. ed. São Paulo: Contexto, 2021. p. 187–219.
BASSO, R. M. A Semântica das Relações Anafóricas entre Eventos. tese de doutorado—[s.l.] Universidade Estadual de Campinas, SP, 2009.
BATES, M. et al. Research in Knowledge Representation for Natural Language Understanding: Bolt, Beranek, and Newman. SIGART Bull., n. 79, p. 30–31, jan. 1982.
BATISTA, C.; DIAS, A. L.; NETO, N. Free resources for forced phonetic alignment in Brazilian Portuguese based on Kaldi toolkit. EURASIP Journal on Advances in Signal Processing, v. 2022, n. 1, p. 11, 19 fev. 2022.
BECKMAN, M. E.; HIRSCHBERG, J.; SHATTUCK-HUFNAGEL, S. The original ToBI system and the evolution of the ToBI framework. Em: JUN, S.-A. (Ed.). Prosodic typology: the phonology of intonation and phrasing. Oxford: Oxford University Press, 2005. p. 9–54.
BELTAGY, I.; PETERS, M. E.; COHAN, A. Longformer: The Long-Document Transformer. CoRR, v. abs/2004.05150, 2020.
BENDER, E. M. Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax. Springer Nature Switzerland AG 2013: Springer Cham, 1959. p. XVII–166
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>
BENDER, E. M. The Power of Linguistics - Unpacking Natural Language Processing Ethics with Emily M. Bender. [Podcast]. Disponível em: <https://www.radicalai.org/e16-emily-bender>. Acesso em: 7 abr. 2023.
BENDER, E. M. et al. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Anais...: FAccT ’21.New York, NY, USA: Association for Computing Machinery, 2021. Disponível em: <https://doi.org/10.1145/3442188.3445922>
BENDER, E. M. You Are Not a Parrot And a chatbot is not a human. And a linguist named Emily M. Bender is very worried what will happen when we forget this. Disponível em: <https://nymag.com/intelligencer/article/ai-artificial-intelligence-chatbots-emily-m-bender.html>. Acesso em: 9 abr. 2023.
BENDER, E. M.; FRIEDMAN, B. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, v. 6, p. 587–604, 2018.
BENGIO, Y. et al. A Neural Probabilistic Language Model. J. Mach. Learn. Res., v. 3, n. null, p. 1137–1155, mar. 2003.
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.
BERTAGLIA, T. F. C.; NUNES, M. DAS G. V. Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization. Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT). Anais...Osaka, Japan: The COLING 2016 Organizing Committee, dez. 2016. Disponível em: <https://aclanthology.org/W16-3916>
BERTAGLIA, T. F. C.; NUNES, M. DAS G. V. Normalização textual de conteúdo gerado por usuário. mathesis—[s.l.] Universidade de São Paulo, 2017.
BERTOLDI, A. Os Limites da Criação Automática de Léxicos Computacionais Baseados em Frames: Um Estudo Contrastivo do Frame Criminal_process (The Limits of the Automatic Creation of Frame-based Computational Lexicons: a Contrastive Study of the Criminal_process Frame) [in Portuguese]. Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology. Anais...2011. Disponível em: <https://aclanthology.org/W11-4510>
BERTSCH, A. et al. Unlimiformer: Long-Range Transformers with Unlimited Length Input. CoRR, v. abs/2305.01625, 2023.
BERWICK, R. C.; CHOMSKY, N. Por que apenas nós? Linguagem e evolução. [s.l.] SciELO-Editora UNESP, 2017.
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.
BIBAL, A. et al. Is Attention Explanation? An Introduction to the Debate. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Dublin, Ireland: Association for Computational Linguistics, 2022. Disponível em: <https://aclanthology.org/2022.acl-long.269>
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.
BICK, E. A dependency-based approach to anaphora annotation. Proceedings of th 9th International Conference on Computational Processing of the Portuguese Language. Anais...Porto Alegre, Brazil: 2010.
BICK, E. S. PFN-PT: A Framenet Annotator for Portuguese: Anotação semântica automática: um novo Framenet para o português. Domínios de Linguagem, v. 16(4)7, p. 1401–1435, 2009.
BIDERMAN, M. T. C. Teoria linguística: linguística quantitativa e computacional. Rio de Janeiro: Martins Fontes, 1978.
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.; LOPER, E. NLTK: The Natural Language Toolkit. Proceedings of the ACL Interactive Poster and Demonstration Sessions. Anais...Barcelona, Spain: Association for Computational Linguistics, jul. 2004. Disponível em: <https://aclanthology.org/P04-3031>
BIRON, T. et al. Automatic detection of prosodic boundaries in spontaneous speech. PLoS ONE, v. 16, n. 5, p. 1–21, maio 2021.
BITTENCOURT JR., J. A. S. Avaliação automática de redação em língua portuguesa empregando redes neurais profundas. mathesis—[s.l.] Universidade Federal de Goiás, 2020.
BLACKBURN, P.; BOS, J. Representation and Inference for Natural Language: A First Course in Computational Semantics. [s.l.] Center for the Study of Language; Information, 2005.
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.
BLOM, J. D. A dictionary of hallucinations. [s.l.] Springer, 2010.
BOBROW, D. G. et al. GUS, a frame-driven dialog system. Artificial Intelligence, v. 8, n. 2, p. 155–173, 1977.
BOERSMA, P.; WEENINK, D. Praat: doing phonetics by computer [Computer program]. Version 6.3.10., 2023. Disponível em: <http://www.praat.org/>
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, F.; FOSTER, R. Linking and extending an open multilingual wordnet. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Sofia, Bulgaria: Association for Computational Linguistics, ago. 2013. Disponível em: <https://aclanthology.org/P13-1133>
BONIFACIO, L. H. et al. mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset., 2021. Disponível em: <https://arxiv.org/abs/2108.13897>
BOWMAN, S. R. et al. A large annotated corpus for learning natural language inference. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Anais...Lisbon, Portugal: Association for Computational Linguistics, set. 2015. Disponível em: <https://aclanthology.org/D15-1075>
BRANDES, N. et al. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinform., v. 38, n. 8, p. 2102–2110, 2022.
BRANDOM, R. B. Articulating Reasons: An Introduction to Inferentialism. Cambridge, Massachusetts, EUA: Harvard University Press, 2001.
BRAUDE, D. A.; SHIMODAIRA, H.; YOUSSEF, A. B. Template-warping based speech driven head motion synthesis. Interspeech. Anais...2013.
BRAUN, H. I. Understanding Scoring Reliability: Experiments in Calibrating Essay Readers. Journal of Educational Statistics, v. 13, n. 1, p. 1–18, 1988.
BREEN, J. JMdict: a Japanese-Multilingual Dictionary. Proceedings of the Workshop on Multilingual Linguistic Resources. Anais...Geneva, Switzerland: COLING, 2004. Disponível em: <https://aclanthology.org/W04-2209>
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.
BREWSTER, C.; WILKS, Y. Ontologies, taxonomies, thesauri:learning from texts. (M. Deegan, Ed.)Proceedings of Use of Computational Linguistics in the Extraction of Keyword Information from Digital Library Content Workshop. Anais...2004. Disponível em: <http://www.cbrewster.com/papers/KeyWord_FMO.pdf>
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.
BRILL, E. A Simple Rule-Based Part of Speech Tagger. Proceedings of the Third Conference on Applied Natural Language Processing. Anais...: ANLC ’92.USA: Association for Computational Linguistics, 1992. Disponível em: <https://doi.org/10.3115/974499.974526>
BRIN, S. Extracting patterns and relations from the world wide web. International workshop on the world wide web and databases. Anais...Springer, 1998.
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 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Anais...2020. 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.
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.
BUOLAMWINI, J.; GEBRU, T. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. (S. A. Friedler, C. Wilson, Eds.)Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Anais...: Proceedings of Machine Learning Research.PMLR, 2018. Disponível em: <https://proceedings.mlr.press/v81/buolamwini18a.html>
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 International Conference on Enterprise Information Systems. Anais...2021.
CABRÉ, M. T. La terminología: representación y comunicación. [s.l.] Editora: Documenta Universitaria, 1999.
CABRÉ, M. T. A Terminologia, uma disciplina em evolução: passado, presente e alguns elementos de futuro. Debate Terminológico. ISSN: 1813-1867, n. 01, 2005.
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. et al. CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese. CoRR, v. abs/2110.15731, 2021.
CANDIDO JUNIOR, A. et al. CORAA ASR: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese. Language Resources & Evaluation, 2022.
CARDOSO, N. Rembrandt - a named-entity recognition framework. Proceedings of the Eighth International Conference on Language Resources and Evaluation. Anais...Istanbul, Turkey: 2012. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2012/summaries/409.html>
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.] Universidade de São Paulo, 2014.
CARL, M.; WAY, A. (EDS.). Recent Advances in Example-Based Machine Translation. [s.l.] Springer Netherlands, 2003.
CARLSON, L.; MARCU, D. Discourse tagging reference manual. ISI Technical Report ISI-TR-545, v. 54, n. 2001, p. 56, 2001.
CARMO, D. et al. PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data. CoRR, v. abs/2008.09144, 2020.
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.
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. Síntese de voz aplicada ao português brasileiro usando aprendizado profundo. {B.S.} thesis—[s.l.] Universidade Tecnológica Federal do Paraná, 2019.
CASANOVA, E. et al. TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese. Language Resources and Evaluation, v. 56, n. 3, p. 1043–1055, 2022.
CASANOVA, E.; SHULBY, C. D.; ALUÍSIO, S. M. Deep learning approaches for speech synthesis and speaker verification. Acoustic communication: an interdisciplinary approach, 2021.
CASELI, H. DE M.; FREITAS, C.; VIOLA, R. Processamento de Linguagem Natural. Em: Tópicos em Gerenciamento de Dados e Informações: Minicursos do SBBD 2022. [s.l.] Sociedade Brasileira de Computação, 2022. p. 1–28.
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, A. T. DE. O português culto falado no Brasil: história do Projeto NURC. Em: PRETI, D.; URBANO, H. (Eds.). A linguagem falada culta na cidade de São Paulo. São Paulo, SP: TAQ/Fapesp, 1990. v. 4 – Estudosp. 141–292.
CASTILHO, A. T. DE. Gramática do Português Brasileiro: fundamentos, perspectivas. Cadernos de Linguística, v. 2, n. 1, p. e252, abr. a2021.
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 2019.
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. b2021. Disponível em: <https://www.aclweb.org/anthology/2021.humeval-1.4>
CASTILHO, S. et al. DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. Proceedings of the Sixth Conference on Machine Translation. Anais...Online: Association for Computational Linguistics, nov. 2021. Disponível em: <https://aclanthology.org/2021.wmt-1.63>
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. 2019. Disponível em: <https://aclanthology.org/W19-8703>
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.
CAVALIERE, P.; ROMEO, G. From Poisons to Antidotes: Algorithms as Democracy Boosters. European Journal of Risk Regulation, v. 13, n. 3, p. 421–442, 2022.
CHALMERS, D. J. Syntactic transformations on distributed representations. Connectionist Natural Language Processing: Readings from Connection Science, p. 46–55, 1992.
CHANDRAN, R. Indigenous groups in NZ, US fear colonisation as AI learns their languages. Disponível em: <https://www.context.news/ai/nz-us-indigenous-fear-colonisation-as-bots-learn-their-languages>. Acesso em: 7 abr. 2023.
CHANG, K.-W. et al. Illinois-Coref: The UI system in the CoNLL-2012 shared task. Joint Conference on EMNLP and CoNLL-Shared Task. Anais...Association for Computational Linguistics, 2012.
CHARPENTIER, F.; STELLA, M. Diphone synthesis using an overlap-add technique for speech waveforms concatenation. ICASSP’86. IEEE International Conference on Acoustics, Speech, and Signal Processing. Anais...IEEE, 1986.
CHE, X. et al. Punctuation Prediction for Unsegmented Transcript Based on Word Vector. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). Anais...Portorož, Slovenia: European Language Resources Association (ELRA), 2016. Disponível em: <https://aclanthology.org/L16-1103>
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, K.; HASEGAWA-JOHNSON, M. A. How prosody improves word recognition. Speech Prosody 2004. Anais...2004.
CHEN, L.-W.; RUDNICKY, A. Exploring Wav2vec 2.0 Fine Tuning for Improved Speech Emotion Recognition. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Anais...IEEE, 2023.
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.
CHILD, R. et al. Generating Long Sequences with Sparse Transformers. CoRR, v. abs/1904.10509, 2019.
CHO, K. et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. (A. Moschitti, B. Pang, W. Daelemans, Eds.)Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL. Anais...ACL, 2014. Disponível em: <https://doi.org/10.3115/v1/d14-1179>
CHOWDHERY, A. et al. PaLM: Scaling Language Modeling with Pathways. CoRR, v. abs/2204.02311, 2022.
CHRISMAN, L. Learning recursive distributed representations for holistic computation. Connection Science, v. 3, n. 4, p. 345–366, 1991.
CHRISTIANO, P. F. et al. Deep Reinforcement Learning from Human Preferences. (I. Guyon et al., Eds.)Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. Anais...2017. Disponível em: <https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html>
CHUNG, Y.-A.; GLASS, J. Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech. Proc. Interspeech 2018. Anais...2018.
CIERI, C.; MILLER, D.; WALKER, K. The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text. Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC04). Anais...Lisbon, Portugal: European Language Resources Association (ELRA), 2004. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2004/pdf/767.pdf>
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>
CLIFTON, A. et al. 100,000 podcasts: A spoken English document corpus. Proceedings of the 28th International Conference on Computational Linguistics. Anais...2020.
COECKELBERGH, M. Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, v. 26, p. 2051–2068, 2020.
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.
COHEN, A. D. et al. LaMDA: Language Models for Dialog Applications. Em: arXiv. [s.l: s.n.].
COHEN, J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, v. 20, n. 1, p. 37–46, 1960.
COLLOBERT, R.; WESTON, J. A unified architecture for natural language processing: deep neural networks with multitask learning. (W. W. Cohen, A. McCallum, S. T. Roweis, Eds.)Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008. Anais...: ACM International Conference Proceeding Series.ACM, 2008. Disponível em: <https://doi.org/10.1145/1390156.1390177>
COLLOVINI, S. et al. Summ-it: Um Corpus Anotado com Informações Discursivas Visando a Sumarização Automática. Proceedings of V Workshop em Tecnologia da Informação e da Linguagem Humana. Anais...Rio de Janeiro, Brasil: 2007.
COLLOVINI, S. et al. Extraction of Relation Descriptors for Portuguese Using Conditional Random Fields. Proceedings of the 14th Ibero-American Conference on Advances in Artificial Intelligence. Anais...Santiago de Chile: 2014.
COLLOVINI, S. et al. IberLEF 2019 Portuguese Named Entity Recognition and Relation Extraction Tasks. [s.l: s.n.].
COMMISSION, E. Proposal for a Regulation laying down harmonised rules on artificial intelligence. Disponível em: < https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence >. Acesso em: 28 ago. 2023.
CONCEIÇÃO, M. C.; ZANOLA, M. T. Terminologia e mediação linguı́stica: métodos, práticas e atividades. Universidade do Algarve Editora, 2020.
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.
CONNEAU, A.; LAMPLE, G. Cross-Lingual Language Model Pretraining. Em: Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., 2019.
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.
COPESTAKE, A. et al. Minimal recursion semantics: An introduction. Research on language and computation, v. 3, p. 281–332, 2005.
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.
COREIXAS, T. Resolução De Correferência E Categorias De Entidades Nomeadas. Dissertação de Mestrado, Pontifı́cia Universidade Católica do Rio Grande do Sul, 2010.
CORMEN, T. et al. Introduction to Algorithms. Em: 2. ed. [s.l.] MIT Press; McGraw-Hill, 2001.
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.
CORTES, C.; VAPNIK, V. Support-Vector Networks. Mach. Learn., v. 20, n. 3, p. 273–297, set. 1995.
CORTIZ, D. et al. A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese. 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/17786>
COSTA, A. et al. A linguistically motivated taxonomy for Machine Translation error analysis. Machine Translation, v. 29, n. 2, p. 127–161, 2015.
COUILLAULT, A. et al. Evaluating corpora documentation with regards to the Ethics and Big Data Charter. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). Anais...Reykjavik, Iceland: European Language Resources Association (ELRA), 2014. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2014/pdf/424_Paper.pdf>
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.
CRISTEA, D.; IDE, N.; ROMARY, L. Veins theory: A model of global discourse cohesion and coherence. Coling-ACL Conference. Anais...1998.
CROFT, W. B.; METZLER, D.; STROHMAN, T. Search engines: Information retrieval in practice. [s.l.] Addison-Wesley, 2010. v. 520
CRUZ, B. S. Concessionária do Metrô de SP é processada por ter câmeras que leem nossas emoções. Disponível em: < https://www.uol.com.br/tilt/noticias/redacao/2018/08/31/concessionaria-do-metro-de-sp-e-processada-por-ter-cameras-que-leem-emocoes.htm >. Acesso em: 29 ago. 2023.
CRUZ, B. S. Racismo Calculado. Disponível em: < https://www.uol.com.br/tilt/reportagens-especiais/como-os-algoritmos-espalham-racismo/#cover >. Acesso em: 29 ago. 2023.
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, 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, V. Natural language processing and logic programming. Journal of Logic Programming, v. 19-20, n. 1, p. 681–714, 1994.
DAI, Z. et al. Transformer-XL: Attentive Language Models beyond a Fixed-Length Context. (A. Korhonen, D. R. Traum, L. Màrquez, Eds.)Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers. Anais...Association for Computational Linguistics, 2019. Disponível em: <https://doi.org/10.18653/v1/p19-1285>
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.
DARPA (ED.). Proceedings of the 3rd Message Understanding Conference (MUC-3). San Diego, EUA: Morgan Kaufmann, 1991.
DE PAIVA, V. et al. An overview of Portuguese wordnets. Proceedings of the 8th Global WordNet Conference (GWC). Anais...2016.
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.
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>
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.
DEMPSEY, P. The teardown: Google Home personal assistant. Engineering & Technology, v. 12, n. 3, p. 80–81, 2017.
DETTMERS, T. et al. QLoRA: Efficient Finetuning of Quantized LLMs. arXiv preprint arXiv:2305.14314, 2023.
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>
DHUMAL DESHMUKH, R.; KIWELEKAR, A. Deep Learning Techniques for Part of Speech Tagging by Natural Language Processing. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). Anais...mar. 2020.
DI GANGI, M. A. et al. MuST-C: a Multilingual Speech Translation Corpus. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Anais...Minneapolis, Minnesota: Association for Computational Linguistics, jun. 2019. Disponível em: <https://aclanthology.org/N19-1202>
DIAS-DA-SILVA, B. C. A face tecnológica dos estudos da linguagem: o processamento automático das lı́nguas naturais. 1996. 272f. tese de doutorado—[s.l.] Tese (Doutorado em Lingüı́stica e Lı́ngua Portuguesa)–Faculdade de Ciências e …, 1996.
DIAS-DA-SILVA, B. C. Wordnet.Br: An Exercise of Human Language Technology Research. Proceedings of the Third International WordNet Conference. Anais...2005. Disponível em: <http://semanticweb.kaist.ac.kr/conference/gwc/pdf2006/6.pdf>
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. R. et al. The automatic content extraction (ace) program-tasks, data, and evaluation. Lrec. Anais...Lisbon, 2004.
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.
DONG, Q. et al. A Survey for In-context Learning. CoRR, v. abs/2301.00234, 2023.
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.
DU BOIS, J. W. et al. Santa Barbara corpus of spoken American English. Parts 1–4. Philadelphia: Linguistic Data Consortium, 2000--2005.
DU BOIS, J. W. et al. Discourse transcription. Santa Barbara: Department of Linguistics, University of California, 1992. v. 4
DURAN, M. S. et al. The Dawn of the Porttinari Multigenre Treebank: Introducing its Journalistic Portion. Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Anais...Porto Alegre, RS, Brasil: SBC, 2023.
DURAN, M. S.; ALUÍSIO, S. M. Propbank-Br: a Brazilian Treebank Annotated with Semantic Role Labels. Proceedings of the 8th International Conference on Language Resources and Evaluation - LREC. Anais...2012.
EBDEN, P.; SPROAT, R. The Kestrel TTS text normalization system. Natural Language Engineering, v. 21, p. 333–353, maio 2014.
EIJCK, J. VAN; UNGER, C. Computational Semantics with Functional Programming. [s.l.] Cambridge University Press, 2010.
EISENSTEIN, J. Introduction to Natural Language Processing. [s.l.] The MIT Press, 2019.
EKMAN, P. An argument for basic emotions. Cognition and Emotion, v. 6, n. 3-4, p. 169–200, 1992.
EL AYADI, M.; KAMEL, M. S.; KARRAY, F. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern recognition, v. 44, n. 3, p. 572–587, 2011.
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.
EMPOLI, G. DA. Os engenheiros do caos: Como as fake news, as teorias da conspiração e os algoritmos estão sendo utilizados para disseminar ódio, medo e influenciar eleições. [s.l.] Vestígio Editora, 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.
Euromatrix. Survey of Machine Translation Evaluation. [s.l.] Statistical; Hybrid Machine Translation Between All European Languages. Euromatrix, dez. 2007.
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>
FAN, A.; LEWIS, M.; DAUPHIN, Y. Hierarchical Neural Story Generation. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Melbourne, Australia: Association for Computational Linguistics, jul. 2018. Disponível em: <https://aclanthology.org/P18-1082>
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.
FAYEK, H. M. Speech Processing for Machine Learning: Filter banks, Mel-Frequency Cepstral Coefficients (MFCCs) and What’s In-Between., 2016. Disponível em: <https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html>
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>
FEIJÓ, D. DE V.; MOREIRA, V. P. Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks. CoRR, v. abs/2007.09757, 2020.
FELLBAUM, C. (EDITOR). 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.
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, E. R.; SANTOS, C. N. DOS; MILIDIÚ, R. L. Latent trees for coreference resolution. Computational Linguistics, 2014.
FERRADEIRA, J. E. DE S. Resolução de anáfora pronominal. mathesis—[s.l.] Universidade Nova de Lisboa; Dissertação de Mestrado, Universidade Nova de Lisboa, 1993.
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.
FILLMORE, C. J. et al. Frame semantics and the nature of language. Annals of the New York Academy of Sciences: Conference on the origin and development of language and speech. Anais...New York, 1976.
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.
FINE, K. Truthmaker semantics. A Companion to the Philosophy of Language, p. 556–577, 2017.
FIRTH, J. R. The technique of semantics. Transactions of the philological society, v. 34, n. 1, p. 36–73, a1957.
FIRTH, J. R. A synopsis of linguistic theory 1930–1955. [s.l.] Blackwell, 1957b. p. 1–32
FLEISS, J. L. Measuring nominal scale agreement among many raters. Psychological Bulletin, v. 76, n. 5, p. 378–382, 1971.
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. B. Resolução de correferências em língua portuguesa: pessoa, local e organização. Dissertação de Mestrado, Pontifı́cia Universidade Católica do Rio Grande do Sul, 2014.
FONSECA, E. B. et al. Summ-it++: an enriched version of the summ-it corpus. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). Anais...2016.
FONSECA, E. B. Resolução de correferência nominal usando semântica em língua portuguesa. tese de doutorado—[s.l.] Pontifícia Universidade Católica do Rio Grande do Sul; Pontifı́cia Universidade Católica do Rio Grande do Sul, 2018.
FONSECA, E. B.; VIEIRA, R.; VANIN, A. Dealing With Imbalanced Datasets For Coreference Resolution. Proceedings of The Twenty-Eighth International Flairs Conference. Anais...2015.
FONSECA, E. B.; VIEIRA, R.; VANIN, A. Adapting an Entity Centric Model for Portuguese Coreference Resolution. Portorož, Slovenia, b2016.
FONSECA, E. B.; VIEIRA, R.; VANIN, A. CORP: Coreference Resolution for Portuguese., a2016.
FONSECA, E. B.; VIEIRA, R.; VANIN, A. A. Coreference Resolution In Portuguese: Detecting Person, Location And Organization. Journal of the Brazilian Computational Intelligence Society, v. 12, n. 2, p. 86–97, 2014.
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.
FONSECA, E. R.; ROSA, J. L. G. Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging. Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology. Anais...2013. Disponível em: <https://aclanthology.org/W13-4811>
FONSECA, E. R.; ROSA, J. L.; ALUÍSIO, S. M. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society, v. 21, n. 1, p. 32–38, fev. 2015.
FONSECA, E.; VANIN, A.; VIEIRA, R. Mention clustering to improve portuguese semantic coreference resolution. International Conference on Applications of Natural Language to Information Systems. Anais...Springer, 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.
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.
FREITAS, C. et al. Relation detection between named entities: report of a shared task. Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Anais...Boulder, Colorado: 2009.
FREITAS, C. et al. Second HAREM: Advancing the State of the Art of Named Entity Recognition in Portuguese. Proceedings of the International Conference on Language Resources and Evaluation. Anais...Valletta, Malta: 2010. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2010/summaries/412.html>
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, C. et al. Tagsets and Datasets: Some Experiments Based on Portuguese Language. (A. Villavicencio et al., Eds.)Computational Processing of the Portuguese Language. Anais...Cham: Springer International Publishing, 2018.
FREITAS, C. Linguística Computacional. [s.l.] Editora Parábola, 2022.
FREITAS, C.; ROCHA, P.; BICK, E. Floresta sintá (c) tica: bigger, thicker and easier. International Conference on Computational Processing of the Portuguese Language. Anais...Springer, 2008.
FREITAS, C.; SANTOS, D. Gender Depiction in Portuguese: Distant reading Brazilian and Portuguese literature. 2nd Annual Conference of Computational Literary Studies. Anais...2023. Disponível em: <https://www.linguateca.pt/Diana/download/FreitasSantos2023-2ndCCLS.pdf>
FREITAS, C.; SOUZA, E. Sujeito oculto às claras: uma abordagem descritivo-computacional / Omitted subjects revealed: a quantitative-descriptive approach. REVISTA DE ESTUDOS DA LINGUAGEM, v. 29, n. 2, p. 1033–1058, 2021.
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.
FYFE, S. et al. Apophenia, theory of mind and schizotypy: perceiving meaning and intentionality in randomness. Cortex, v. 44, n. 10, p. 1316–1325, 2008.
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>
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.
GAO, T.; YAO, X.; CHEN, D. SimCSE: Simple Contrastive Learning of Sentence Embeddings. (M.-F. Moens et al., Eds.)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.emnlp-main.552>
GARCIA, M.; GAMALLO, P. An Entity-Centric Coreference Resolution System for Person Entities with Rich Linguistic Information. Proceedings of 25th International Conference on Computational Linguistics. Anais...Dublin, Ireland: 2014. Disponível em: <http://aclweb.org/anthology/C/C14/C14-1070.pdf>
GAUY, M. M.; FINGER, M. Pretrained audio neural networks for Speech emotion recognition in Portuguese. Proceedings of the Workshop on Automatic Speech Recognition for Spontaneous and Prepared Speech & Speech Emotion Recognition in Portuguese co-located with 15th edition of the International Conference on the Computational Processing of Portuguese (PROPOR 2022). Anais...2022.
GEVA, M.; GUPTA, A.; BERANT, J. Injecting Numerical Reasoning Skills into Language Models. (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.89>
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, 2019.
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, 2019.
GOLUB, G. H.; REINSCH, C. Singular Value Decomposition and Least Squares Solutions. [s.l.] Numer. Math 14, 1970. p. 403–420
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.
GONÇALO OLIVEIRA, H. Beyond the automatic construction of a lexical ontology for Portuguese: resources developed in the scope of Onto.PT. Proceedings of Workshop on Tools and Resources for Automatically Processing Portuguese and Spanish. Anais...: TorPorEsp.São Carlos, SP, Brasil: BDBComp, 2014. Disponível em: <http://www.lbd.dcc.ufmg.br/colecoes/torporesp/2014/004.pdf>
GONÇALO OLIVEIRA, H.; GOMES, P. ECO and Onto-PT: a flexible approach for creating a Portuguese Wordnet automatically. Language Resources and Evaluation, v. 48, n. 2, p. 373–393, 2014.
GONÇALVES, M. et al. Avaliação de recursos computacionais para o português. Linguamática, v. 12, n. 2, p. 51–68, 2020.
GONÇALVES, S. C. L. Projeto ALIP (Amostra Linguística do Interior Paulista) e banco de dados Iboruna: 10 anos de contribuição com a descrição do português brasileiro. Revista Estudos Linguísticos, v. 48, n. 1, p. 276–297, dez. 2019.
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.
GONG, Z. et al. Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network. (S. Muresan, P. Nakov, A. Villavicencio, Eds.)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022. Anais...Association for Computational Linguistics, 2022. Disponível em: <https://doi.org/10.18653/v1/2022.acl-long.408>
GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. [s.l.] MIT Press, 2016. v. 1
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>
GRICE, H. P. Logic and Conversation. Em: Syntax and Semantics: Vol. 3: Speech Acts. [s.l.] Academic Press, 1975.
GRIES, S. C. Estatística com R para a Linguística. [s.l.] FALE/ UFMG, 2019.
GRIS, L. R. S. et al. Bringing NURC/SP to digital life: the role of open-source automatic speech recognition models. Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional. Anais...Porto Alegre, RS, Brasil: SBC, 2022. Disponível em: <https://sol.sbc.org.br/index.php/eniac/article/view/22793>
GRIS, L. R. S. et al. Evaluating OpenAI’s Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person., 2023. Disponível em: <https://arxiv.org/abs/2305.14580>
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>
GROSZ, B. J.; JOSHI, A. K.; WEINSTEIN, S. Centering: A framework for modelling the local coherence of discourse. IRCS Technical Reports Series, 1995.
GROSZ, B. J.; SIDNER, C. L. Attention, intentions, and the structure of discourse. Computational linguistics, v. 12, n. 3, p. 175–204, 1986.
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.
GRUBER, T. R. Siri, A Virtual Personal Assistant-Bringing Intelligence to the Interface. Semantic Technologies Conference. Anais...2009.
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, S. S. et al. Characterizing Toxicity on Facebook Comments in Brazil. Proceedings of the Brazilian Symposium on Multimedia and the Web. Anais...2020.
GULATI, A. et al. Conformer: Convolution-augmented Transformer for Speech Recognition. CoRR, v. abs/2005.08100, 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.
GURURANGAN, S. et al. Annotation Artifacts in Natural Language Inference Data. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Anais...New Orleans, Louisiana: Association for Computational Linguistics, jun. 2018. Disponível em: <https://aclanthology.org/N18-2017>
GURURANGAN, S. et al. Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Anais...Online: Association for Computational Linguistics, jul. 2020. Disponível em: <https://aclanthology.org/2020.acl-main.740>
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.
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.
HALL, J. A Probabilistic Part-of-Speech Tagger with Suffix Probabilities. tese de doutorado—[s.l: s.n.].
HALLIDAY, M. A. K.; MATTHIESSEN, C. M. I. M. Construing Experience Through Meaning: A Language Based Approach to Cognition. [s.l.] Continuum, 1999.
HAPKE, H.; HOWARD, C.; LANE, H. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python. [s.l.] Manning, 2019.
HARRIS, Z. S. Distributional Structure. Word, v. 10, n. 2-3, p. 146–162, 1954.
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, H. et al. Achieving Human Parity on Automatic Chinese to English News Translation. arXiv preprint 1803.05567, 2018.
HAVASI, C.; SPEER, R.; ALONSO, J. ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. Recent Advances in Natural Language Processing. Anais...Borovets, Bulgaria: To appear, 2007.
HE, K. et al. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. Anais...IEEE Computer Society, 2016. Disponível em: <https://doi.org/10.1109/CVPR.2016.90>
HE, P. et al. Deberta: decoding-Enhanced Bert with Disentangled Attention. 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. Anais...OpenReview.net, 2021. Disponível em: <https://openreview.net/forum?id=XPZIaotutsD>
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.
HEIKKILÄ, M. Why you shouldn’t trust AI search engines. Disponível em: <https://www.technologyreview.com/2023/02/14/1068498/why-you-shouldnt-trust-ai-search-engines/>. Acesso em: 9 abr. 2023.
HEIKKILÄ, M. The viral AI avatar app Lensa undressed me—without my consent. Disponível em: < https://www.technologyreview.com/2022/12/12/1064751/the-viral-ai-avatar-app-lensa-undressed-me-without-my-consent/>. Acesso em: 28 ago. 2023.
HEIM, I. File Change Semantics and the Familiarity Theory of Definiteness. Em: Formal Semantics. [s.l.] Wiley-Blackwell, 2008. p. 223–248.
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.
HOCHREITER, S. Untersuchungen zu dynamischen neuronalen Netzen. Diploma, Technische Universität München, v. 91, n. 1, p. 31, 1991.
HOCHREITER, S.; SCHMIDHUBER, J. Long Short-Term Memory. Neural Computation, v. 9, n. 8, p. 1735–1780, nov. 1997.
HOFFMANN, J. et al. Training Compute-Optimal Large Language Models. CoRR, v. abs/2203.15556, 2022.
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.
HOLTZMAN, A. et al. The Curious Case of Neural Text Degeneration. ICLR. Anais...OpenReview.net, 2020. Disponível em: <http://dblp.uni-trier.de/db/conf/iclr/iclr2020.html#HoltzmanBDFC20>
HORA, N. DA. Coded Bias: linguagem acessível para entender vieses em algoritmos. Disponível em: < https://mittechreview.com.br/coded-bias-linguagem-acessivel-para-entender-vieses-em-algoritmos/>. Acesso em: 7 abr. 2023.
HORA, N. DA. Ética em IA: a pergunta que não estamos fazendo. Disponível em: <https://mittechreview.com.br/etica-em-ia-a-pergunta-que-nao-estamos-fazendo/>. Acesso em: 7 abr. 2023.
HORNIK, K.; STINCHCOMBE, M. B.; WHITE, H. Multilayer feedforward networks are universal approximators. Neural Networks, v. 2, n. 5, p. 359–366, 1989.
HORSMANN, T.; ZESCH, T. Assigning Fine-grained PoS Tags based on High-precision Coarse-grained Tagging. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. Anais...Osaka, Japan: The COLING 2016 Organizing Committee, dez. 2016. Disponível em: <https://aclanthology.org/C16-1032>
HOU, Y.; MARKERT, K.; STRUBE, M. A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Anais...Doha, Qatar: 2014. Disponível em: <http://aclweb.org/anthology/D/D14/D14-1222.pdf>
HOULSBY, N. et al. Parameter-Efficient Transfer Learning for NLP. (K. Chaudhuri, R. Salakhutdinov, Eds.)Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Anais...: Proceedings of Machine Learning Research.PMLR, 2019. Disponível em: <http://proceedings.mlr.press/v97/houlsby19a.html>
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.
HOWARD, J.; RUDER, S. Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Melbourne, Australia: Association for Computational Linguistics, jul. 2018. Disponível em: <^5^>
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, E. J. et al. LoRA: Low-Rank Adaptation of Large Language Models. The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. Anais...OpenReview.net, 2022. Disponível em: <https://openreview.net/forum?id=nZeVKeeFYf9>
HU, M.; LIU, B. Mining Opinion Features in Customer Reviews. Proceedings of the 19th National Conference on Artifical Intelligence. Anais...2004.
HUANG, J.-T.; HASEGAWA-JOHNSON, M.; SHIH, C. Unsupervised prosodic break detection in Mandarin speech. Proc. Speech Prosody 2008. Anais...2008.
HUANG, X.; ACERO, A.; HON, H. W. Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. [s.l.] Prentice Hall PTR, 2001.
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.
IGNAT, O. et al. A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models., 2023. Disponível em: <https://arxiv.org/abs/2305.12544>
ILARI, R.; GERALDI, J. W. Semântica. [s.l.] Ética, 1985.
INFOBASE. Inteligência Artificial e a perpetuação do racismo. Disponível em: <https://infobase.com.br/inteligencia-artificial-e-a-perpetuacao-do-racismo/>. Acesso em: 28 ago. 2023.
ITO, K. The LJ speech dataset. https://keithito.com/LJ-Speech-Dataset/, 2017.
IVGI, M.; SHAHAM, U.; BERANT, J. Efficient Long-Text Understanding with Short-Text Models. Transactions of the Association for Computational Linguistics, v. 11, p. 284–299, 2023.
JACKSON, P.; MOULINIER, I. Natural Language Processing for Online Applications – Text retrieval, extraction and categorization. [s.l.] John Benjamins, 2002.
JAHAN, M. S.; OUSSALAH, M. A systematic review of hate speech automatic detection using natural language processing. Neurocomputing, 2023.
JAIN, S.; WALLACE, B. C. Attention is not Explanation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Anais...Minneapolis, Minnesota: Association for Computational Linguistics, 2019. Disponível em: <https://aclanthology.org/N19-1357>
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.
JEON, J. H.; LIU, Y. Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Anais...Suntec, Singapore: Association for Computational Linguistics, ago. 2009. Disponível em: <https://aclanthology.org/P09-1061>
JI, Z. et al. Survey of Hallucination in Natural Language Generation. ACM Comput. Surv., v. 55, n. 12, mar. 2023.
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.
JIN, X. et al. Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Anais...Seattle, United States: Association for Computational Linguistics, jul. 2022. Disponível em: <https://aclanthology.org/2022.naacl-main.351>
JOHNSON, K. Acoustic and Auditory Phonetics. [s.l.] Wiley, 2011.
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.
JOOS, M. Description of language design. Journal of Acoustical Society of America - JASA, v. 22, p. 701–708, 1950.
JOSHI, M. et al. BERT for Coreference Resolution: Baselines and Analysis. 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...Hong Kong, China: Association for Computational Linguistics, nov. 2019. Disponível em: <https://aclanthology.org/D19-1588>
JOSHI, M. et al. SpanBERT: Improving Pre-training by Representing and Predicting Spans. Transactions of the Association for Computational Linguistics, v. 8, p. 64–77, 2020.
JOYCE, J. M. Kullback-Leibler Divergence. Em: LOVRIC, M. (Ed.). International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. p. 720–722.
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.
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.
KANTAYYA, S. Coded Bias. Disponível em: < https://www.codedbias.com>. Acesso em: 7 abr. 2023.
KE, Z. et al. Continual Pre-training of Language Models., 2023. Disponível em: <https://arxiv.org/abs/2302.03241>
KENEDY, E.; OTHERO, G. DE Á. Para conhecer sintaxe. São Paulo: Contexto, 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>
KIANPOUR, M.; WEN, S.-F. Timing Attacks on Machine Learning: State of the Art. Intelligent Systems Conference. Anais...Springer, 2020.
KILGARRIFF, A. I Don’t Believe in Word Senses. Computers and the Humanities, 1997.
KILGARRIFF, A. Thesauruses for Natural Language Processing. Proceedings of Natural Language Processing and Knowledge Engineering. Anais...2003. Disponível em: <https://www.kilgarriff.co.uk/Publications/2003-K-Beijing-thes4NLP.pdf>
KIM, J. et al. Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search. arXiv preprint arXiv:2005.11129, 2020.
KIM, J.; KONG, J.; SON, J. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. International Conference on Machine Learning. Anais...PMLR, 2021.
KIPPER, K.; DANG, H. T.; PALMER, M. Class-Based Construction of a Verb Lexicon. Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence. Anais...AAAI Press, 2000.
KIRSTAIN, Y.; RAM, O.; LEVY, O. Coreference Resolution without Span Representations. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Anais...2021.
KLATT, D. H. Software for a cascade/parallel formant synthesizer. the Journal of the Acoustical Society of America, v. 67, n. 3, p. 971–995, 1980.
KLIE, J.-C. et al. The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation. Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations. Anais...Santa Fe, USA: Association for Computational Linguistics, 2018. Disponível em: <http://tubiblio.ulb.tu-darmstadt.de/106270/>
KNUTH, D. E. Fundamental Algorithms. The Art of Computer Programming. 3. ed. [s.l.] Addison-Wesley, 1997. v. 1
KOCH, I. G. V. O texto e a construção do sentido. 7. ed. Campinas, SP: Contexto, 2003.
KOCH, I. G. V.; TRAVAGLIA, L. Texto e coerência. 13. ed. [s.l.] Cortez, 2012.
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>
KOIZUMI, Y. et al. Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations. arXiv preprint arXiv:2303.01664, b2023.
KOIZUMI, Y. et al. LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus. arXiv preprint arXiv:2305.18802, a2023.
KOJIMA, T. et al. Large Language Models are Zero-Shot Reasoners. NeurIPS. Anais...2022. Disponível em: <http://papers.nips.cc/paper\_files/paper/2022/hash/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html>
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.
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.
KRINGS, H. P. Repairing Texts: Empirical Investigations of Machine Translation Post-editing Processes. [s.l.] Kent State University Press, 2001.
KRIPPENDORFF, K. Estimating the Reliability, Systematic Error and Random Error of Interval Data. Educational and Psychological Measurement, v. 30, n. 1, p. 61–70, 1970.
KRUSE, J. S.; BARBOSA, P. A. Alinha-PB: a phonetic aligner for Brazilian Portuguese. Journal of Communication and Information Systems, v. 36, n. 1, p. 192–199, dez. 2021.
KUDO, T. Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Melbourne, Australia: Association for Computational Linguistics, jul. 2018. Disponível em: <https://aclanthology.org/P18-1007>
KUMAR, D. et al. Understanding the Behaviors of Toxic Accounts on Reddit. Proceedings of the ACM Web Conference 2023. Anais...2023.
KUMAWAT, D.; JAIN, V. POS Tagging Approaches: A Comparison. International Journal of Computer Applications, v. 118, n. 6, p. 32–38, maio 2015.
KUO, Y. et al. Community-Based Game Design: Experiments on Social Games for Commonsense Data Collection. Proceedings of the ACM SIGKDD Workshop on Human Computation. Anais...: HCOMP ’09.New York, NY, USA: Association for Computing Machinery, 2009. Disponível em: <https://doi.org/10.1145/1600150.1600154>
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.
KYLE, K. K. J. F. S.; JOSE, K. A. C. Y. B.; SOTELO, S. M. Char2wav: End-to-end speech synthesis. International Conference on Learning Representations, workshop. Anais...2017.
LAN, Z. et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. 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=H1eA7AEtvS>
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.
LEACOCK, C. et al. Automated Grammatical Error Detection for Language Learners. [s.l.] Morgan; Claypool Publishers, 2010.
LEAL, S. E. et al. NILC-Metrix: assessing the complexity of written and spoken language in Brazilian Portuguese. CoRR, v. abs/2201.03445, 2021.
LÉCHELLE, W.; GOTTI, F.; LANGLAIS, P. WiRe57: A Fine-Grained Benchmark for Open Information Extraction. arXiv preprint arXiv:1809.08962, 2018.
LEE, H. et al. Stanford’s multi-pass sieve coreference resolution system at the CoNLL-2011 shared task. Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task. Anais...2011.
LEE, H. et al. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics, v. 39, n. 4, p. 885–916, 2013.
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, K. et al. End-to-end neural coreference resolution. arXiv preprint arXiv:1707.07045, 2017.
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.
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.; SCHNEIDER, J. M. Fine-Grained Named Entity Recognition in Legal Documents. (M. Acosta et al., Eds.)Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference. Anais...2019.
LENAT, D. B.; GUHA, R. V. Building large knowledge-based systems: representation and inference in the Cyc project. [s.l.] Addison-Wesley, 1989.
LESK, M. The seven ages of information retrieval., 1995. Disponível em: <https://archive.ifla.org/VI/5/op/udtop5/udt-op5.pdf>
LESTER, B.; AL-RFOU, R.; CONSTANT, N. The Power of Scale for Parameter-Efficient Prompt Tuning. (M.-F. Moens et al., Eds.)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.emnlp-main.243>
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, a2020. Disponível em: <https://doi.org/10.18653/v1/2020.acl-main.703>
LEWIS, P. S. H. et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. (H. Larochelle et al., Eds.)Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Anais...b2020. Disponível em: <https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html>
LGPD. Lei Geral de Proteção de Dados Pessoais (LGPD). Disponível em: <https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm>. Acesso em: 9 abr. 2023.
LI, J. et al. Molweni: A challenge multiparty dialogues-based machine reading comprehension dataset with discourse structure. arXiv preprint arXiv:2004.05080, 2020.
LI, P. et al. Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models. arXiv preprint arXiv:2304.03271, a2023.
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, R. et al. StarCoder: may the source be with you! CoRR, v. abs/2305.06161, b2023.
LI, W. W. et al. BERT Is Not The Count: Learning to Match Mathematical Statements with Proofs. (A. Vlachos, I. Augenstein, Eds.)Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023, Dubrovnik, Croatia, May 2-6, 2023. Anais...Association for Computational Linguistics, c2023. Disponível em: <https://aclanthology.org/2023.eacl-main.260>
LI, X. L.; LIANG, P. Prefix-Tuning: Optimizing Continuous Prompts for Generation. (C. Zong et al., Eds.)Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.acl-long.353>
LIANG, X. et al. Contrastive Demonstration Tuning for Pre-trained Language Models. (Y. Goldberg, Z. Kozareva, Y. Zhang, Eds.)Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022. Anais...Association for Computational Linguistics, 2022. Disponível em: <https://aclanthology.org/2022.findings-emnlp.56>
LIKERT, R. A Technique for the Measurement of Attitudes. [s.l.] Archives of Psychology, 1932.
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.-H. et al. Rich prosodic information exploration on spontaneous Mandarin speech. 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP). Anais...Tianjin: 2016.
LIN, C.-H. et al. Hierarchical prosody modeling for Mandarin spontaneous speech. The Journal of the Acoustical Society of America, v. 145, n. 4, p. 2576–2596, 2019.
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, J.; NOGUEIRA, R.; YATES, A. Pretrained Transformers for Text Ranking: BERT and Beyond. arXiv preprint arXiv:2010.06467, 2020.
LIU, B. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 2012.
LIU, H.; SINGH, P. Commonsense Reasoning in and Over Natural Language. (M. Gh. Negoita, R. J. Howlett, L. C. Jain, Eds.)Knowledge-Based Intelligent Information and Engineering Systems. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
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...2019.
LIU, Y. et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR, v. abs/1907.11692, 2019.
LIU, Y. et al. Multilingual Denoising Pre-training for Neural Machine Translation. Trans. Assoc. Comput. Linguistics, v. 8, p. 726–742, 2020.
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>
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.
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>
LOPE, J.; GRAÑA, M. An ongoing review of speech emotion recognition. Neurocomputing, 2023.
LOPES, L. et al. PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model. Proceedings of the Language Resources and Evaluation Conference. Anais...Marseille, France: European Language Resources Association, jun. 2022. Disponível em: <https://aclanthology.org/2022.lrec-1.715>
LOPES, L. et al. Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese. (A. Britto, K. V. Delgado, Eds.)Proceedings of the 12th Brazilian Conference on Intelligent Systems (BRACIS). Anais...2023.
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.
LOVINS, J. B. Development of a stemming algorithm. Mech. Transl. Comput. Linguistics, v. 11, n. 1-2, p. 22–31, 1968.
LUCY, L.; BAMMAN, D. Gender and Representation Bias in GPT-3 Generated Stories. Proceedings of the Third Workshop on Narrative Understanding. Anais...Virtual: Association for Computational Linguistics, jun. 2021. Disponível em: <https://aclanthology.org/2021.nuse-1.5>
LUDUSAN, B.; SYNNAEVE, G.; DUPOUX, E. Prosodic boundary information helps unsupervised word segmentation. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Anais...2015.
LUO, X. On Coreference Resolution Performance Metrics. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Anais...Vancouver, Canada: 2005.
LUONG, T.; PHAM, H.; MANNING, C. D. Effective Approaches to Attention-based Neural Machine Translation. (L. Màrquez et al., Eds.)Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. Anais...The Association for Computational Linguistics, 2015. Disponível em: <https://doi.org/10.18653/v1/d15-1166>
LYONS, J. Semantics: Volume 2. [s.l.] Cambridge university press, 1977. v. 2
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.
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.
MANN, W. C.; THOMPSON, S. A. Rhetorical structure theory: Toward a functional theory of text organization. Text-interdisciplinary Journal for the Study of Discourse, v. 8, n. 3, p. 243–281, 1988.
MANNING, C. D.; SCHÜTZE, H.; RAGHAVAN, P. Introduction to information retrieval. [s.l.] Cambridge University Press Cambridge, 2008.
MARCACINI, R. M.; CANDIDO JUNIOR, A.; CASANOVA, E. Overview of the Automatic Speech Recognition for Spontaneous and Prepared Speech & Speech Emotion Recognition in Portuguese (SE&R) Shared-tasks at PROPOR 2022. Proceedings of the Workshop on Automatic Speech Recognition for Spontaneous and Prepared Speech & Speech Emotion Recognition in Portuguese co-located with 15th edition of the International Conference on the Computational Processing of Portuguese (PROPOR 2022). Anais...2022.
MARCU, D. From local to global coherence: A bottom-up approach to text planning. AAAI/IAAI. Anais...Citeseer, 1997.
MARCU, D.; CARLSON, L.; WATANABE, M. The automatic translation of discourse structures. 1st Meeting of the North American Chapter of the Association for Computational Linguistics. Anais...2000.
MARCUSCHI, L. A. Produção textual, análise de gêneros e compreensão. [s.l.] Parábola Ed., 2008.
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, 2022.
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, 2022.
MARKOV, A. A. The theory of algorithms. Trudy Matematicheskogo Instituta Imeni VA Steklova, v. 42, p. 3–375, 1954.
MARNEFFE, M.-C. DE et al. Universal Dependencies. Computational Linguistics, v. 47, n. 2, p. 255–308, jun. 2021.
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, H. Sobre a estabilidade do significado em Wittgenstein. Veredas, v. 4, n. 2, p. 19–42, 2000.
MARTINS, H. Três Caminhos na Filosofia da Linguagem. Em: Introdução à Linguística. Volume III. [s.l.] Editora Cortez, 2004.
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, R.; NUNES, M. DAS G. V.; HASEGAWA, R. Curupira: A Functional Parser for Brazilian Portuguese. (N. J. Mamede et al., Eds.)Computational Processing of the Portuguese Language. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
MARTSCHAT, S.; STRUBE, M. Latent Structures for Coreference Resolution. Transactions of the Association for Computational Linguistics, v. 3, p. 405–418, 2015.
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.
MATTHEWS, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure, v. 405, n. 2, p. 442–451, 1975.
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.
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. et al. A base de dados lexical e a interface web do TeP 2.0: thesaurus eletrônico para o Português do Brasil. Proceedings of the XIV Brazilian Symposium on Multimedia and the Web. Anais...Salvador, Brazil: 2008.
MAZIERO, E. G. Análise retórica com base em grande quantidade de dados. tese de doutorado—[s.l.] Universidade de São Paulo, 2016.
MAZIERO, E. G.; HIRST, G.; PARDO, T. A. S. Adaptation of discourse parsing models for the Portuguese language. 2015 Brazilian Conference on Intelligent Systems (BRACIS). Anais...IEEE, 2015.
MAZIERO, E. G.; JORGE, M. L. DEL R. C.; PARDO, T. A. S. Identifying Multidocument Relations. NLPCS, v. 7, p. 60–69, 2010.
MAZIERO, E. G.; PARDO, T. A. S. Automatic Identification of Multi-document Relations. Proceedings of the PROPOR 2012 PhD and MSc/MA Dissertation Contest, p. 1–8, 2012.
MAZUMDER, M. et al. Multilingual spoken words corpus. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). Anais...2021.
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.
MCCANN, B. et al. Learned in Translation: Contextualized Word Vectors. Proceedings of the 31st International Conference on Neural Information Processing Systems. Anais...: NIPS’17.Red Hook, NY, USA: Curran Associates Inc., 2017.
MCCRAE, J. P. et al. English WordNet 2019 An Open-Source WordNet for English. Proceedings of the 10th Global Wordnet Conference. Anais...Wroclaw, Poland: Global Wordnet Association, jul. 2019. Disponível em: <https://aclanthology.org/2019.gwc-1.31>
MCDONALD, R. et al. Universal Dependency Annotation for Multilingual Parsing. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Anais...Sofia, Bulgaria: Association for Computational Linguistics, ago. 2013. Disponível em: <https://aclanthology.org/P13-2017>
MELLO, H.; RASO, T.; ALMEIDA FERRARI, L. DE. C-ORAL–Brasil II: Corpus de referência do português brasileiro falado informal., no prelono prelo.
MELO, G. DE; WEIKUM, G. Towards a universal wordnet by learning from combined evidence. Proceedings of the 18th ACM conference on Information and knowledge management. Anais...2009.
MENDES, R. B.; OUSHIRO, L. Mapping Paulistano Portuguese: the SP2010 Project. Proceedings of the VIIth GSCP International Conference: Speech and Corpora. Anais...Firenze, Italy: Fizenze University Press, 2012.
MEYER, C. F. et al. The world wide web as linguistic corpus. Em: Corpus Analysis. [s.l.] Brill Rodopi, 2003. p. 241–254.
MIIKKULAINEN, R.; DYER, M. G. Natural Language Processing With Modular Pdp Networks and Distributed Lexicon. Cognitive Science, v. 15, n. 3, p. 343–399, 1991.
MIKOLOV, T. et al. Efficient Estimation of Word Representations in Vector Space., a2013. Disponível em: <https://arxiv.org/abs/1301.3781>
MIKOLOV, T. et al. Distributed Representations of Words and Phrases and their Compositionality. (C. J. C. Burges et al., Eds.)Advances in Neural Information Processing Systems. Anais...Curran Associates, Inc., b2013. Disponível em: <https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf>
MINSKY, M. A framework for representing knowledge. The psychology of computer vision, 1975.
MITKOV, R. The Oxford handbook of Computational Linguistics. [s.l.] Oxford University Press, 2003.
MITKOV, R. 21 Discourse Processing. The handbook of computational linguistics and natural language processing, p. 599, 2010.
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...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>.
MOORE, R. K. Spoken language processing: Piecing together the puzzle. Speech Communication, v. 49, n. 5, p. 418–435, 2007.
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.
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.; 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.
MULLER, P. et al. Manuel d’annotation en relations de discours du projet annodis., 2012.
MUNIZ, M. C. M. A construção de recursos linguístico-computacionais para o português do Brasil: o projeto Unitex-PB. mathesis—[s.l.] Instituto de Ciências Matemáticas e de Computação - Universidade de São Paulo - ICMC/USP, 2004.
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.
NAMIUTI, C. O Corpus Anotado do Português Histórico: um avanço para as pesquisas em Linguística Histórica do Português. Revista Virtual de Estudos da Linguagem, v. 2, p. 1–9, ago. 2004.
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, G. et al. Hate speech detection using brazilian imageboards. Proceedings of the 25th Brazillian Symposium on Multimedia and the Web. Anais...2019.
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.
NAVIGLI, R.; PONZETTO, S. P. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial intelligence, v. 193, p. 217–250, 2012.
NECO, R. P.; FORCADA, M. L. Asynchronous translations with recurrent neural nets. Proceedings of International Conference on Neural Networks (ICNN’97). Anais...1997.
NETO, F. A. R. 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.
NEVES, M. H. DE M. Texto e gramática. [s.l.] Contexto, 2013.
NEWMAN, N. et al. Reuters institute digital news report 2020. [s.l.] Report of the Reuters Institute for the Study of Journalism, 2020.
NG, V.; CARDIE, C. Improving machine learning approaches to coreference resolution. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Anais...Association for Computational Linguistics, 2002.
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.
NIJKAMP, E. et al. ProGen2: Exploring the Boundaries of Protein Language Models. CoRR, v. abs/2206.13517, 2022.
NIVRE, J. et al. The CoNLL 2007 Shared Task on Dependency Parsing. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). Anais...Prague, Czech Republic: Association for Computational Linguistics, jun. 2007. Disponível em: <https://aclanthology.org/D07-1096>
NOGUEIRA, R. et al. Document expansion by query prediction. arXiv preprint arXiv:1904.08375, 2019.
NOORALAHZADEH, F.; ØVRELID, L. Syntactic Dependency Representations in Neural Relation Classification. Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP. Anais...Melbourne, Australia: Association for Computational Linguistics, jul. 2018. Disponível em: <https://aclanthology.org/W18-2907>
NOZAKI, J. et al. End-to-end Speech-to-Punctuated-Text Recognition. Proc. Interspeech 2022. Anais...2022.
NUNES, M. DAS G. V. et al. O uso de interlı́ngua para comunicação via Internet: a decodificação UNL-português. Revista Tecnologia da Informação, v. 3, n. 1, p. 49–55, 2003.
NUNES, P. LEVANTAMENTO REVELA QUE 90,5% DOS PRESOS POR MONITORAMENTO FACIAL NO BRASIL SÃO NEGROS. Disponível em: < https://www.intercept.com.br/2019/11/21/presos-monitoramento-facial-brasil-negros/>. Acesso em: 28 ago. 2023.
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.
O’NEIL, C. Algoritmos de Destruição em Massa. [s.l.] Editora Rua do Sabão, 2021.
OCH, F. J.; NEY, H. The Alignment Template Approach to Statistical Machine Translation. Computational Linguistics, v. 30, n. 4, p. 417–449, dez. 2004.
OECD. The OECD Framework for the Classification of AI systems. Disponível em: < https://wp.oecd.ai/app/uploads/2022/02/Classification-2-pager-1.pdf>. Acesso em: 28 ago. 2023.
OLIVEIRA, F. S. et al. CML-TTS: A Multilingual Dataset for Speech Synthesis in Low-Resource Languages. International Conference on Text, Speech, and Dialogue. Anais...Springer, 2023.
OLIVEIRA JR., M. NURC Digital: um protocolo para a digitalização, anotação, arquivamento e disseminação do material do Projeto da Norma Urbana Linguística Culta (NURC). CHIMERA: Revista de Corpus de Lenguas Romances y Estudios Lingüísticos, v. 3, n. 2, p. 149–174, set. 2016.
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. 2022.
OLIVEIRA, M. R. DE. Manual de Linguística. Em: MARTELOTTA, M. E. (Ed.). São Paulo: Contexto, 2008. p. 193–204.
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.
OPENAI. ChatGPT: OpenA’s conversational AI model. Disponível em: <https://openai.com/blog/chatgpt/>. Acesso em: 7 abr. 2023.
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.
OSBORNE, T.; GERDES, K. The status of function words in dependency grammar: A critique of Universal Dependencies (UD). Glossa: a journal of general linguistics (2016-2021), jan. 2019.
OSGOOD, C. E.; SUCI, G. J.; TENENBAUM, P. H. The Measurement of meaning. Urbana: University of Illinois Press, 1957.
OSTENDORF, M.; PRICE, P.; SHATTUCK-HUFNAGEL, S. The Boston University Radio news corpus., 1995. Disponível em: <https://doi.org/10.35111/Z7XK-Z229>
OUYANG, L. et al. Training language models to follow instructions with human feedback. NeurIPS. Anais...2022. Disponível em: <http://papers.nips.cc/paper\_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html>
OVCHINNIKOVA, E. Integration of World Knowledge for Natural Language Understanding. [s.l.] Atlantis Press, 2012.
OVERWIJK, A.; XIONG, C.; CALLAN, J. ClueWeb22: 10 Billion Web Documents with Rich Information. (E. Amigó et al., Eds.)SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Anais...ACM, 2022. Disponível em: <https://doi.org/10.1145/3477495.3536321>
ÖZSEVEN, T. Investigation of the effect of spectrogram images and different texture analysis methods on speech emotion recognition. Applied Acoustics, v. 142, p. 70–77, 2018.
PAGE, E. B.; PETERSEN, N. S. The Computer Moves into Essay Grading: Updating the Ancient Test. Phi Delta Kappan, v. 76, p. 561–565, mar. 1995.
PĂIŞ, V.; TUFIŞ, D. Capitalization and punctuation restoration: a survey. Artificial Intelligence Review, v. 55, p. 1681--1722, 2022.
PALMER, M.; GILDEA, D.; KINGSBURY, P. The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31: 1. Anais...The MIT PressJournals, 2005.
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>
PARDO, T. et al. Porttinari - a Large Multi-genre Treebank for Brazilian Portuguese. 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/17778>
PARDO, T. A. S. Métodos para análise discursiva automática. tese de doutorado—[s.l.] Universidade de São Paulo, 2005.
PARK, D. S. et al. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. Interspeech 2019. Anais...ISCA, set. 2019. Disponível em: <https://doi.org/10.21437%2Finterspeech.2019-2680>
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>
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.; 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.
PERRIGO, B. Disponível em: <https://time.com/6247678/openai-chatgpt-kenya-workers/>. Acesso em: 9 abr. 2023.
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. 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.
PIĘKOS, P.; MALINOWSKI, M.; MICHALEWSKI, H. Measuring and Improving BERTs Mathematical Abilities by Predicting the Order of Reasoning. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Anais...Online: Association for Computational Linguistics, ago. 2021. Disponível em: <https://aclanthology.org/2021.acl-short.49>
PING, W. et al. Deep voice 3: 2000-speaker neural text-to-speech. arXiv preprint arXiv:1710.07654, 2017.
PINHEIRO, V. et al. InferenceNet.Br: Expression of Inferentialist Semantic Content of the Portuguese Language. (T. A. S. Pardo et al., Eds.)Computational Processing of the Portuguese Language. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
PIRES, R. et al. Sabiá: Portuguese Large Language Models. Anais da XII Brazilian Conference on Intelligent Systems - BRACIS 2023. Anais...2023. Disponível em: <https://arxiv.org/abs/2304.07880>
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.
POESIO, M.; STUCKARDT, R.; VERSLEY, Y. Anaphora Resolution: Algorithms, Resources, and Applications. 1. ed. [s.l.] Springer, 2016.
PONTIKI, M. et al. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of the 8th International Workshop on Semantic Evaluation. Anais...2014.
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.
POSNER, J.; RUSSELL, J. A.; PETERSON, B. S. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and psychopathology, v. 17, n. 3, p. 715–734, 2005.
PRADHAN, S. et al. CoNLL-2011 shared task: Modeling unrestricted coreference in ontonotes. Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task. Anais...Portland, Oregon: Association for Computational Linguistics, 2011.
PRADHAN, S. et al. CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Conference on Natural Language Learning - Shared Task. Anais...Jeju Island, Korea: 2012.
PRADHAN, S. et al. Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Anais...Baltimore, MD, USA: 2014. Disponível em: <http://aclweb.org/anthology/P/P14/P14-2006.pdf>
PRATAP, V. et al. Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters., a2020. Disponível em: <https://arxiv.org/abs/2007.03001>
PRATAP, V. et al. MLS: A Large-Scale Multilingual Dataset for Speech Research. Proc. Interspeech 2020, p. 2757–2761, b2020.
PROVILKOV, I.; EMELIANENKO, D.; VOITA, E. BPE-Dropout: Simple and Effective Subword Regularization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Anais...Online: Association for Computational Linguistics, jul. 2020.
PURINGTON, A. et al. " Alexa is my new BFF" Social Roles, User Satisfaction, and Personification of the Amazon Echo. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Anais...2017.
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.
QUINTANILHA, I. M.; NETTO, S. L.; BISCAINHO, L. W. P. An open-source end-to-end ASR system for Brazilian Portuguese using DNNs built from newly assembled corpora. Journal of Communication and Information Systems, v. 35, n. 1, p. 230–242, 2020.
RABINER, L. R.; JUANG, B. H. Fundamentals of Speech Recognition. [s.l.] Pearson Education, 1993.
RADEMAKER, A. et al. Universal Dependencies for Portuguese. Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017). Anais...Pisa,Italy: Linköping University Electronic Press, set. 2017. Disponível em: <https://aclanthology.org/W17-6523>
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.
RADFORD, A. et al. Language Models are Unsupervised Multitask Learners. 2019.
RADFORD, A. et al. Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356, 2022.
RADFORD, A.; NARASIMHAN, K. Improving Language Understanding by Generative Pre-Training. 2018.
RAE, J. W. et al. Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR, v. abs/2112.11446, 2021.
RAFFEL, C. et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res., v. 21, p. 140:1–140:67, 2020.
RAHMAN, A.; NG, V. Coreference Resolution with World Knowledge. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Anais...Portland, Oregon, USA: b2011. Disponível em: <http://www.aclweb.org/anthology/P11-1082>
RAHMAN, A.; NG, V. Narrowing the modeling gap: a cluster-ranking approach to coreference resolution. Journal of Artificial Intelligence Research, p. 469–521, a2011.
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.
RANCHHOD, E.; MOTA, C.; BAPTISTA, J. A Computational Lexicon of Portuguese for Automatic Text Parsing. SIGLEX99: Standardizing Lexical Resources. Anais...1999. Disponível em: <https://aclanthology.org/W99-0511>
RAO, K. S.; KOOLAGUDI, S. G.; VEMPADA, R. R. Emotion recognition from speech using global and local prosodic features. International journal of speech technology, v. 16, p. 143–160, 2013.
RASO, T.; MELLO, H. C-ORAL–BRASIL I: corpus de referência do português brasileiro falado informal. Belo Horizonte: Editora UFMG, 2012a.
RASO, T.; MELLO, H. C-ORAL–BRASIL I: corpus de referência do português brasileiro falado informal. A general presentation. Speech and Corpora, p. 16, b2012.
RASO, T.; TEIXEIRA, B.; BARBOSA, P. Modelling automatic detection of prosodic boundaries for Brazilian Portuguese spontaneous speech. Journal of Speech Sciences, v. 9, p. 105–128, set. 2020.
RAU, L. F. Extracting company names from text. Proceedings the Seventh IEEE Conference on Artificial Intelligence Application. Anais...IEEE Computer Society, 1991.
READ, J. et al. Sentence Boundary Detection: A Long Solved Problem? Proceedings of COLING 2012: Posters. Anais...Mumbai, India: The COLING 2012 Organizing Committee, dez. 2012. Disponível em: <https://aclanthology.org/C12-2096>
REAL, L.; FONSECA, E.; GONÇALO OLIVEIRA, H. The ASSIN 2 Shared Task: A Quick Overview. Computational Processing of the Portuguese Language: 14th International Conference, PROPOR 2020, Evora, Portugal, March 2–4, 2020, Proceedings. Anais...Berlin, Heidelberg: Springer-Verlag, 2020. Disponível em: <https://doi.org/10.1007/978-3-030-41505-1_39>
RECASENS, M.; HOVY, E. H. BLANC: Implementing the Rand index for coreference evaluation. Natural Language Engineering, v. 17, n. 4, p. 485–510, 2011.
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>
REIMERS, N.; GUREVYCH, I. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Anais...Association for Computational Linguistics, nov. 2019. Disponível em: <https://arxiv.org/abs/1908.10084>
REIMERS, N.; GUREVYCH, I. Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Anais...Association for Computational Linguistics, nov. 2020. Disponível em: <https://arxiv.org/abs/2004.09813>
RESENDE, G. et al. (Mis)Information Dissemination in WhatsApp: Gathering, Analyzing and Countermeasures. Proceedings of the World Wide Web Conference. Anais...2019.
REVIEW, M. T. Um aplicativo de Inteligência Artificial que “desnudava” mulheres mostra como as deepfakes prejudicam os mais vulneráveis. Disponível em: < https://mittechreview.com.br/um-aplicativo-de-inteligencia-artificial-que-desnudava-mulheres-mostra-como-as-deepfakes-prejudicam-os-mais-vulneraveis/>. Acesso em: 28 ago. 2023.
REYES, A.; ROSSO, P.; BUSCALDI, D. From Humor Recognition to Irony Detection: The Figurative Language of Social Media. Data & Knowledge Engineering, 2012.
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.
RIZZOLATTI, G.; ARBIB, M. A. Language within our grasp. Trends in Neurosciences, v. 21, n. 5, p. 188–194, 1998.
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.
ROCCHIO-JR, J. J. Relevance feedback in information retrieval. The SMART retrieval system: experiments in automatic document processing, 1971.
ROCHA, M. A corpus-based study of anaphora in English and Portuguese, Corpus-based and Computational Approaches to Discourse Anaphora. Em: [s.l.] John Benjamins Publishing Company, 2000. p. 81–94.
RODRIGUES, J. et al. Advancing Neural Encoding of Portuguese with Transformer Albertina PT-. CoRR, v. abs/2305.06721, 2023.
RODRIGUES, R. C. et al. Portuguese Language Models and Word Embeddings: Evaluating on Semantic Similarity Tasks. (P. Quaresma et al., Eds.)Computational Processing of the Portuguese Language. Anais...Springer Nature Switzerland AG: Springer International Publishing, 2020.
ROMERA-PAREDES, B.; TORR, P. H. S. An embarrassingly simple approach to zero-shot learning. (F. R. Bach, D. M. Blei, Eds.)Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. Anais...: JMLR Workshop e Conference Proceedings.JMLR.org, 2015. Disponível em: <http://proceedings.mlr.press/v37/romera-paredes15.html>
RONCARATI, C. As cadeias do texto: construindo sentidos. [s.l.] Parábola, 2010.
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.
RUPPENHOFER, J. et al. FrameNet II: Extended theory and practice. [s.l: s.n.].
RUSSEL, S. Human Compatible Artificial Intelligence and the Problem of Control. [s.l.] Penguin Books, 2019.
RUSSELL, M. A. Mineração de Dados da Web Social. Primeira edição ed. São Paulo: O’Reilly Novatec, 2011.
SAEKI, T. et al. Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech., 2023. Disponível em: <https://arxiv.org/abs/2210.15447>
SAG, I. A. et al. Multiword Expressions: A Pain in the Neck for NLP. Conference on Intelligent Text Processing and Computational Linguistics. Anais...2002. Disponível em: <https://api.semanticscholar.org/CorpusID:1826481>
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.
SAI, A. B.; MOHANKUMAR, A. K.; KHAPRA, M. M. A Survey of Evaluation Metrics Used for NLG Systems. ACM Comput. Surv., v. 55, n. 2, p. 26:1–26:39, 2023.
SALESKY, E. et al. The multilingual tedx corpus for speech recognition and translation. arXiv preprint arXiv:2102.01757, 2021.
SALOMÃO, M. M. M. FrameNet Brasil: A work in progress. Calidoscópio, v. 7, p. 171–182, 2009.
SALTON, G.; ALLAN, J. Text retrieval using the vector processing model. dez. 1994.
SALTON, G.; MCGILL, M. J. Introduction to Modern Information Retrieval. [s.l.] McGraw-Hill, 1983.
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. 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.
SANH, V. et al. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR, v. abs/1910.01108, 2019.
SANTANA, B. P. Morfologia ornamental: as vogais temáticas do português brasileiro o Unitex-PB. mathesis—Curitiba, PR: Universidade Federal do Paraná, Setor de Ciências Humanas, Programa de Pós-Graduação em Letras, 2019.
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, A. A. et al. O teste de Cloze na avaliação da compreensão em leitura. Psicologia: reflexão e crı́tica, v. 15, p. 549–560, 2002.
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. Avaliação conjunta. Em: SANTOS, D. (Ed.). Avaliação conjunta: um novo paradigma no processamento computacional da língua portuguesa. Lisboa, Portugal: IST Press, 2007. p. 1–12.
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...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. 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, 2021.
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...2019.
SANTOS, J. et al. De-identification of clinical notes using contextualized language models and a token classifier. Brazilian Conference on Intelligent Systems. Anais...Springer, 2021.
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...2020.
SANTOS, V. G. et al. CORAA NURC-SP Minimal Corpus: a manually annotated corpus of Brazilian Portuguese spontaneous speech. Proc. IberSPEECH 2022. Anais...2022.
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, C. DA S. Da Abordagem do Léxico em Livros Didáticos de Língua Portuguesa: os Anos Finais do Ensino Fundamental. mathesis—Brasília: UnB, 2019.
SARMENTO, L.; PINTO, A. S.; CABRAL, L. REPENTINO – a wide-scope gazetteer for entity recognition in portuguese. Proceedings of International Workshop on Computational Processing of the Portuguese Language. Anais...Springer, 2006.
SARTORI, L.; THEODOROU, A. A Sociotechnical Perspective for the Future of AI: Narratives, Inequalities, and Human Control. Ethics and Inf. Technol., v. 24, n. 1, mar. 2022.
SCAO, T. L. et al. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. CoRR, v. abs/2211.05100, 2022.
SCARTON, C. E.; ALUISIO, S. M. Towards a cross-linguistic VerbNet-style lexicon for Brazilian portuguese. Workshop on Creating Cross-language Resources for Disconnected Languages and Styles - CREDISLAS. Anais...ELRA, 2012.
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, abr. 2010.
SCHANK, R. C. et al. MARGIE: Memory Analysis Response Generation, and Inference on English. IJCAI. Anais...1973.
SCHICK, T.; SCHÜTZE, H. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. (P. Merlo, J. Tiedemann, R. Tsarfaty, Eds.)Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.eacl-main.20>
SCHMID, H. Part-of-Speech Tagging with Neural Networks., 1994. Disponível em: <https://arxiv.org/abs/cmp-lg/9410018>
SCHMIDHUBER, J.; HEIL, S. Sequential neural text compression. IEEE Transactions on Neural Networks, v. 7, n. 1, p. 142–146, 1996.
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>
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.
SCHUSTER, M.; NAKAJIMA, K. Japanese and Korean voice search. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Anais...2012.
SEARA, I. Estudo Estatístico dos Fonemas do Português Brasileiro Falado na Capital de Santa Catarina para Elaboração de Frases Foneticamente Balanceadas. tese de doutorado—[s.l.] Dissertação de Mestrado, Universidade Federal de Santa Catarina …, 1994.
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>
SELLARS, W. Inference and Meaning. Mind, v. 62, n. 247, p. 313–338, 1953.
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...a2016. Disponível em: <https://arxiv.org/abs/1511.06709>
SENNRICH, R.; HADDOW, B.; BIRCH, A. Neural Machine Translation of Rare Words with Subword Units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Anais...Berlin, Germany: Association for Computational Linguistics, ago. b2016. Disponível em: <https://aclanthology.org/P16-1162>
SENO, E. R. M. RHeSumaRST: um sumarizador automático de estruturas RST. mathesis—[s.l.] Universidade Federal de São Carlos, 2005.
SERRA, C. R. Realização e percepção de fronteiras prosódicas no português do Brasil: fala espontânea e leitura. tese de doutorado—Rio de Janeiro: Universidade Federal do Rio de Janeiro, 2009.
SHANNON, C. E. Prediction and entropy of printed English. Bell System Technical Journal, v. 30, n. 1, p. 50–64, 1951.
SHAOWEI, Z. et al. Survey of Supervised Joint Entity Relation Extraction Methods. Journal of Frontiers of Computer Science & Technology, v. 16, n. 4, 2022.
SHAPIRO, S. C. SNePS: A Logic for Natural Language Understanding and Commonsense Reasoning. Em: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language. Cambridge, MA, USA: MIT Press, 2000. p. 175–195.
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, J. et al. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Anais...IEEE, 2018.
SHERMIS, M. D.; BURSTEIN, J. Handbook of Automated Essay Evaluation: Current Applications and New Directions. [s.l.] Routledge/Taylor & Francis Group, 2013.
SHI, Z.; LIPANI, A. Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner., 2023. Disponível em: <https://arxiv.org/abs/2305.01711>
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.
SHTERIONOV, D. et al. Human versus Automatic Quality Evaluation of NMT and PBSMT. Machine Translation, v. 32, n. 3, p. 217–235, 2018.
SIDDHI, D.; VERGHESE, J. M.; BHAVIK, D. Survey on various methods of text to speech synthesis. International Journal of Computer Applications, v. 165, n. 6, 2017.
SIDNER, C. A progress report on the discourse and reference components of PAL. [s.l.] Massachusetts Institute of Tech Cambridge Artificial Intelligence LAB, 1978.
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, E.; PARDO, T.; ROMAN, N. Etiquetagem morfossintática multigênero para o português do Brasil segundo o modelo Üniversal Dependencies̈. Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Anais...Porto Alegre, RS, Brasil: SBC, 2023. Disponível em: <https://sol.sbc.org.br/index.php/stil/article/view/25438>
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, 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. DA. Resolução de correferência em múltiplos documentos utilizando aprendizado não supervisionado. Dissertação de Mestrado, Universidade de São Paulo, 2011.
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.
SIMÕES, A.; GUINOVART, X. G. Bootstrapping a Portuguese WordNet from Galician, Spanish and English Wordnets. IberSPEECH Conference. Anais...2014. Disponível em: <https://api.semanticscholar.org/CorpusID:10377782>
SINGH, P. et al. Open Mind Common Sense: Knowledge Acquisition from the General Public. (R. Meersman, Z. Tari, Eds.)On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE. Anais...Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.
SINGH, Y. B.; GOEL, S. A systematic literature review of speech emotion recognition approaches. Neurocomputing, 2022.
Slator 2019 Language Industry Market Report. p. 33, 2019.
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, G.; RUSTAGI, I. Mitigating Bias in Artificial Intelligence: An Equity Fluent Leadership Playbook. [s.l.] Berkeley Haas Center for Equity, Gender; Leadership, 2020.
SMITH, K. S. On Integrating Discourse in Machine Translation. Proceedings of the Third Workshop on Discourse in Machine Translation. Anais...2017.
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/>
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.
SOON, W. M.; NG, H. T.; LIM, C. Y. A Machine Learning Approach to Coreference Resolution of Noun Phrases. Computational Linguistics, v. 27, n. 4, p. 521–544, 2001.
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, 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. DE. Construção e avaliação de um treebank padrão ouro. Mestrado—[s.l.] PUC-Rio, 2023.
SOUZA, E. DE; FREITAS, C. Explorando variações no tagset e na anotação Universal Dependencies (UD) para Português: Possibilidades e resultados com base no treebank PetroGold. Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Anais...Association for Computational Linguistics, 2023.
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, 2020.
SOUZA, J. W. DA C. Descrição linguística da complementaridade para a sumarização automática multidocumento. mathesis—[s.l.] Universidade Federal de São Carlos, 2015.
SOUZA, J. W. DA C. Aprofundamento da caracterização linguı́stico-computacional da complementaridade em um corpus jornalı́stico multidocumento. tese de doutorado—[s.l.] Universidade Federal de São Carlos, 2019.
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...2011.
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.
SPEER, R.; CHIN, J.; HAVASI, C. ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. CoRR, v. abs/1612.03975, 2016.
STAB, C. et al. Argumentation Mining in Persuasive Essays and Scientific Articles from the Discourse Structure Perspective. ArgNLP. Anais...2014.
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.
STEVENS, S. S. A Scale for the Measurement of the Psychological Magnitude Pitch. Acoustical Society of America Journal, v. 8, n. 3, p. 185, jan. 1937.
STIENNON, N. et al. Learning to summarize with human feedback. (H. Larochelle et al., Eds.)Advances in Neural Information Processing Systems. Anais...Curran Associates, Inc., 2020. Disponível em: <https://proceedings.neurips.cc/paper_files/paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf>
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>
SUCHANEK, F. M.; KASNECI, G.; WEIKUM, G. Yago: a core of semantic knowledge. Proceedings of the 16th international conference on World Wide Web. Anais...2007.
SUNKARA, M. et al. Multimodal Semi-Supervised Learning Framework for Punctuation Prediction in Conversational Speech. Proc. Interspeech 2020. Anais...2020.
SUNKARA, M. et al. Neural Inverse Text Normalization. CoRR, v. abs/2102.06380, 2021.
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>
TABOADA, M.; MANN, W. C. Rhetorical structure theory: Looking back and moving ahead. Discourse studies, v. 8, n. 3, p. 423–459, 2006.
TACHIBANA, H.; UENOYAMA, K.; AIHARA, S. Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention. arXiv preprint arXiv:1710.08969, 2017.
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.
TAN, X. et al. A survey on neural speech synthesis. arXiv preprint arXiv:2106.15561, 2021.
TANAKA, E. et al. Cem Mil Podcasts: A Spoken Portuguese Document Corpus. arXiv preprint arXiv:2209.11871, 2022.
TANG, Y. et al. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning. CoRR, v. abs/2008.00401, 2020.
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
TAYLOR, R. et al. Galactica: A Large Language Model for Science. CoRR, v. abs/2211.09085, 2022.
TAYLOR, W. L. “Cloze procedure”: A new tool for measuring readability. Journalism quarterly, v. 30, n. 4, p. 415–433, 1953.
TEIXEIRA, B. H. F. Detecção automática de fronteiras prosódicas na fala espontânea. tese de doutorado—Belo Horizonte: Universidade Federal de Minas Gerais, 2022.
TEIXEIRA, B. H. F.; MITTMAN, M. M. Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech. Revista de Estudos da Linguagem, v. 26, n. 4, p. 1455–1488, 2018.
TEIXEIRA, B.; BARBOSA, P.; RASO, T. Automatic Detection of Prosodic Boundaries in Brazilian Portuguese Spontaneous Speech. (A. Villavicencio et al., Eds.)Computational Processing of the Portuguese Language. Anais...Cham: Springer International Publishing, 2018.
TEIXEIRA, J. P. et al. Phonetic Events from the Labeling the European Portuguese DataBase for Speech Synthesis, FEUP/IPBDB. Seventh European Conference on Speech Communication and Technology. Anais...2001.
TEIXEIRA, J. P.; FREITAS, D.; FUJISAKI, H. Prediction of Fujisaki model’s phrase commands. Eighth European Conference on Speech Communication and Technology. Anais...2003.
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.
TESNIÈRE, L. Eléments de Syntaxe Structurale. Paris: Klincksieck, 1959.
THOPPILAN, R. et al. LaMDA: Language Models for Dialog Applications. CoRR, v. abs/2201.08239, 2022.
TIRRELL, L. Toxic Speech: Inoculations and Antidotes. The Southern Journal of Philosophy, 2018.
TOKUDA, K. et al. Speech parameter generation algorithms for HMM-based speech synthesis. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100). Anais...IEEE, 2000.
TOLLES, J.; MEURER, W. J. Logistic Regression: Relating Patient Characteristics to Outcomes. JAMA, v. 316, n. 5, p. 533–534, 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. Copa 2014 FrameNet Brasil: a frame-based trilingual electronic dictionary for the Football World Cup. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations. Anais...Dublin, Ireland: Dublin City University; Association for Computational Linguistics, ago. 2014. Disponível em: <https://aclanthology.org/C14-2003>
TORRENT, T. T.; ELLSWORTH, M. Behind the Labels: Criteria for Defining Analytical Categories in FrameNet Brasil. Veredas-Revista de Estudos Linguisticos, v. 17, n. 1, p. 44–66, 2013.
TOUVRON, H. et al. LLaMA: Open and Efficient Foundation Language Models. CoRR, v. abs/2302.13971, 2023.
TRAJANO, D.; BORDINI, R. H.; VIEIRA, R. OLID-BR: offensive language identification dataset for Brazilian Portuguese. Language Resources and Evaluation, 2023.
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.
UNESCO, D. G. Recomendação sobre a Ética da Inteligência Artificial. Disponível em: < https://unesdoc.unesco.org/ark:/48223/pf0000381137_por >. Acesso em: 28 ago. 2023.
UNICEF. Declaração Universal dos Direitos Humanos. Disponível em: < https://www.unicef.org/brazil/declaracao-universal-dos-direitos-humanos>. Acesso em: 28 ago. 2023.
USZKOREIT, H.; LOMMEL, A. Multidimensional Quality Metrics: A New Unified Paradigm for Human and Machine Translation Quality Assessment. [s.l: s.n.].
UZÊDA, V. R.; PARDO, T. A. S.; NUNES, M. G. V. A comprehensive comparative evaluation of RST-based summarization methods. ACM Transactions on Speech and Language Processing (TSLP), v. 6, n. 4, p. 1–20, 2010.
VALLE, R. et al. Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis. arXiv preprint arXiv:2005.05957, 2020.
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...2022.
VARGAS, F. A.; PARDO, T. A. S. Aspect clustering methods for sentiment analysis. Proceedings of International conference on computational processing of the Portuguese language. Anais...Springer, 2018.
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.
VASWANI, A. et al. Attention is All you Need. (I. Guyon et al., Eds.)Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. Anais...2017. Disponível em: <https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html>
VEAUX, C. et al. CSTR VCTK corpus: English multi-speaker corpus for CSTR voice cloning toolkit. University of Edinburgh. The Centre for Speech Technology Research (CSTR), 2017.
VIEIRA, F. E.; FARACO, C. A. Texto e discurso. Escrever na universidade. [s.l.] Parábola, 2019.
VIEIRA, R. et al. Coreference and anaphoric relations of demonstrative noun phrases in multilingual corpus. Anaphora Processing: linguistic, cognitive and computational modeling, p. 385–403, 2005.
VIEIRA, R.; GONÇALVES, P. N.; SOUZA, J. G. C. DE. Processamento computacional de anáfora e correferência. Revista de Estudos da Linguagem, v. 16, n. 1, 2012.
VILAIN, M. et al. A model-theoretic coreference scoring scheme. Proceedings of the 6th Conference on Message understanding. Anais...Columbia, Maryland: 1995.
VILAR, D. et al. Error Analysis of Statistical Machine Translation Output. Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC06). Anais...Genoa, Italy: European Language Resources Association (ELRA), 2006. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2006/pdf/413_pdf.pdf>
VRANDEČIĆ, D.; KRÖTZSCH, M. Wikidata: a free collaborative knowledgebase. Communications of the ACM, v. 57, n. 10, p. 78–85, 2014.
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>
WAGNER, J. et al. Dawn of the transformer era in speech emotion recognition: closing the valence gap. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
WALLIS, S. Completing Parsed Corpora. Em: ABEILLÉ, A. (Ed.). Treebanks: Building and Using Parsed Corpora. Dordrecht: Springer Netherlands, 2003. p. 61–71.
WANG, A. et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Anais...Brussels, Belgium: Association for Computational Linguistics, nov. 2018. Disponível em: <[2](https://aclanthology.org/W18-5446/)>
WANG, A. et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. Advances in Neural Information Processing Systems. Anais...2019.
WANG, B.; KOMATSUZAKI, A. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax, 2021.
WANG, C. et al. Covost: A diverse multilingual speech-to-text translation corpus. arXiv preprint arXiv:2002.01320, 2020.
WANG, C. et al. Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. arXiv preprint arXiv:2101.00390, a2021.
WANG, C. et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers. arXiv preprint arXiv:2301.02111, 2023.
WANG, C.; WU, A.; PINO, J. Covost 2 and massively multilingual speech-to-text translation. arXiv preprint arXiv:2007.10310, 2020.
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, W. Y.; GEORGILA, K. Automatic detection of unnatural word-level segments in unit-selection speech synthesis. 2011 IEEE Workshop on Automatic Speech Recognition & Understanding. Anais...IEEE, 2011.
WANG, Y. et al. Tacotron: A fully end-to-end text-to-speech synthesis model. arXiv preprint arXiv:1703.10135, 2017.
WANG, Y. et al. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. (M.-F. Moens et al., Eds.)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021. Anais...Association for Computational Linguistics, b2021. Disponível em: <https://doi.org/10.18653/v1/2021.emnlp-main.685>
WANI, T. M. et al. A comprehensive review of speech emotion recognition systems. IEEE Access, v. 9, p. 47795–47814, 2021.
WASSERMAN, S.; FAUST, K. Social network analysis: Methods and applications. [s.l.] Cambridge university press, 1994.
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, J. et al. Emergent Abilities of Large Language Models. Trans. Mach. Learn. Res., v. 2022, b2022.
WEI, J. et al. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS. Anais...a2022. Disponível em: <http://papers.nips.cc/paper\_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html>
WERBOS, P. J. Backpropagation through time: what it does and how to do it. Proc. IEEE, v. 78, n. 10, p. 1550–1560, 1990.
WIEGREFFE, S.; PINTER, Y. Attention is not not Explanation. (K. Inui et al., Eds.)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...Hong Kong, China: Association for Computational Linguistics, nov. 2019. Disponível em: <https://aclanthology.org/D19-1002>
WIGHTMAN, C. W.; OSTENDORF, M. Automatic recognition of prosodic phrases. [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, v. 1, p. 321–324, 1991.
WILLIAMS, I. et al. Contextual speech recognition in end-to-end neural network systems using beam search. 2018. Disponível em: <https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2416.pdf>
WOLF, T. et al. Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Anais...Online: Association for Computational Linguistics, out. 2020. Disponível em: <https://www.aclweb.org/anthology/2020.emnlp-demos.6>
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.
WU, Y. et al. Memorizing Transformers. The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. Anais...OpenReview.net, 2022. Disponível em: <https://openreview.net/forum?id=TrjbxzRcnf->
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
XIE, S. M. et al. An Explanation of In-context Learning as Implicit Bayesian Inference. The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. Anais...OpenReview.net, 2022. Disponível em: <https://openreview.net/forum?id=RdJVFCHjUMI>
XIONG, R. et al. On Layer Normalization in the Transformer Architecture. Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event. Anais...: Proceedings of Machine Learning Research.PMLR, 2020. Disponível em: <http://proceedings.mlr.press/v119/xiong20b.html>
XU, W.; RUDNICKY, A. Can artificial neural networks learn language models? Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000). Anais...2000.
XU, Y. et al. Hard Sample Aware Prompt-Tuning. (A. Rogers, J. L. Boyd-Graber, N. Okazaki, Eds.)Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. Anais...Association for Computational Linguistics, 2023. Disponível em: <https://aclanthology.org/2023.acl-long.690>
XUE, L. et al. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. (K. Toutanova et al., Eds.)Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021. Anais...Association for Computational Linguistics, 2021. Disponível em: <https://doi.org/10.18653/v1/2021.naacl-main.41>
YAMAGUCHI, A. et al. Frustratingly Simple Pretraining Alternatives to Masked Language Modeling. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Anais...Online; Punta Cana, Dominican Republic: Association for Computational Linguistics, nov. 2021. Disponível em: <https://aclanthology.org/2021.emnlp-main.249>
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, J.-H. et al. Enriching Mandarin speech recognition by incorporating a hierarchical prosody model. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Anais...2011. Disponível em: <https://doi.org/10.1109/ICASSP.2011.5947492>
YANG, M. et al. Learning ASR pathways: A sparse multilingual ASR model., 2023. Disponível em: <https://arxiv.org/abs/2209.05735>
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, X. et al. An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming. Proceeding of Association for Computational Linguistics. Anais...2008.
YANG, Z. et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding. (H. M. Wallach et al., Eds.)Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. Anais...2019. Disponível em: <https://proceedings.neurips.cc/paper/2019/hash/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html>
YI, J.; TAO, J. Self-attention Based Model for Punctuation Prediction Using Word and Speech Embeddings. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p. 7270–7274, 2019.
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...2021. 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, 2021.
ZE, H.; SENIOR, A.; SCHUSTER, M. Statistical parametric speech synthesis using deep neural networks. 2013 ieee international conference on acoustics, speech and signal processing. Anais...IEEE, 2013.
ZELASKO, P. et al. Punctuation Prediction Model for Conversational Speech. (B. Yegnanarayana, Ed.)Interspeech 2018, 19th Annual Conference of the International Speech Communication Association, Hyderabad, India, 2-6 September 2018. Anais...ISCA, 2018. Disponível em: <https://doi.org/10.21437/Interspeech.2018-1096>
ZELENKO, D.; AONE, C.; RICHARDELLA, A. Kernel methods for relation extraction. Journal of machine learning research, v. 3, n. Feb, p. 1083–1106, 2003.
ZEMAN, D. Reusable Tagset Conversion Using Tagset Drivers. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08). Anais...Marrakech, Morocco: European Language Resources Association (ELRA), 2008. Disponível em: <http://www.lrec-conf.org/proceedings/lrec2008/pdf/66_paper.pdf>
ZEMAN, D.; RESNIK, P. Cross-Language Parser Adaptation between Related Languages. Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages. Anais...2008. Disponível em: <https://aclanthology.org/I08-3008>
ZEN, H. et al. LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech. Proc. Interspeech 2019, p. 1526–1530, 2019.
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.
ZEWDU, A.; YITAGESU, B. Part of speech tagging: a systematic review of deep learning and machine learning approaches. Journal of Big Data, v. 9, jan. 2022.
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.; 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>
ZHAO, J. et al. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Anais...New Orleans, Louisiana: Association for Computational Linguistics, jun. 2018. Disponível em: <https://aclanthology.org/N18-2003>
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.
ZHAO, W. X. et al. A Survey of Large Language Models. CoRR, v. abs/2303.18223, 2023.
ZHOU, C. et al. LIMA: Less Is More for Alignment. CoRR, v. abs/2305.11206, 2023.
ZIEGLER, D. M. et al. Fine-Tuning Language Models from Human Preferences. CoRR, v. abs/1909.08593, 2019.
ZIN, K. K. Hidden Markov model with rule based approach for part of speech tagging of Myanmar language. International Conference on Intelligent Cloud Computing. Anais...2009. Disponível em: <https://api.semanticscholar.org/CorpusID:63473605>
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...1998.