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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval).

, , , , , and . SemEval@NAACL-HLT, page 75-86. Association for Computational Linguistics, (2019)

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Cross-Lingual and Low-Resource Sentiment Analysis.. Columbia University, USA, (2019)The ARIEL-CMU Systems for LoReHLT18., , , , , , , , , and 20 other author(s). CoRR, (2019)SMARTies: Sentiment Models for Arabic Target entities., and . EACL (1), page 1002-1013. Association for Computational Linguistics, (2017)Scoring Persuasive Essays Using Opinions and their Targets., , and . BEA@NAACL-HLT, The Association for Computer Linguistics, (2015)SemEval-2017 Task 4: Sentiment Analysis in Twitter, , and . (2019)cite arxiv:1912.00741Comment: sentiment analysis, Twitter, classification, quantification, ranking, English, Arabic.Cross-lingual sentiment transfer with limited resources., , , , and . Mach. Transl., 32 (1-2): 143-165 (2018)SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)., , , , , and . SemEval@NAACL-HLT, page 75-86. Association for Computational Linguistics, (2019)SemEval-2017 Task 4: Sentiment Analysis in Twitter., , and . SemEval@ACL, page 502-518. Association for Computational Linguistics, (2017)Sentence-Level and Document-Level Sentiment Mining for Arabic Texts., , , and . ICDM Workshops, page 1114-1119. IEEE Computer Society, (2010)The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction., , , , and . ANLP@EMNLP, page 160-164. Association for Computational Linguistics, (2014)