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NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets.

, , and . SemEval@NAACL-HLT, page 321-327. The Association for Computer Linguistics, (2013)

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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition., and . CoRR, (2017)Detecting concept relations in clinical text: Insights from a state-of-the-art model., , , , and . J. Biomed. Informatics, 46 (2): 275-285 (2013)SemEval-2015 Task 10: Sentiment Analysis in Twitter., , , , , and . SemEval@NAACL-HLT, page 451-463. The Association for Computer Linguistics, (2015)NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews., , , and . SemEval@COLING, page 437-442. The Association for Computer Linguistics, (2014)Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task., , , , , , , , , and 6 other author(s). J. Am. Medical Informatics Assoc., 25 (10): 1274-1283 (2018)Computational Modeling of Stereotype Content in Text., , and . Frontiers Artif. Intell., (2022)Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection., , , and . NAACL-HLT, page 2672-2686. Association for Computational Linguistics, (2022)WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art., and . LREC, European Language Resources Association (ELRA), (2018)Generalized Features: Their Application to Classification., and . AAAI/IAAI, page 985. AAAI Press / The MIT Press, (2002)