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SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums.

, , , , , and . SemEval@NAACL-HLT, page 860-869. Association for Computational Linguistics, (2019)

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SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums., , , , , and . SemEval@NAACL-HLT, page 860-869. Association for Computational Linguistics, (2019)Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection., , , , , , and . SemEval@NAACL-HLT, page 1041-1046. Association for Computational Linguistics, (2019)A Sentiment Treebank and Morphologically Enriched Recursive Deep Models for Effective Sentiment Analysis in Arabic., , , , and . ACM Trans. Asian Low Resour. Lang. Inf. Process., 16 (4): 23:1-23:21 (2017)ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets., , , , and . CoRR, (2019)OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model., , , , , , , , , and 1 other author(s). SemEval@ACL, page 603-610. Association for Computational Linguistics, (2017)A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis., , , , , and . ACM Trans. Inf. Syst., 35 (1): 7:1-7:21 (2016)AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language., , , , , and . ACM Trans. Asian Low Resour. Lang. Inf. Process., 16 (4): 25:1-25:20 (2017)Tanbih: Get To Know What You Are Reading., , , , , , , , , and 3 other author(s). EMNLP/IJCNLP (3), page 223-228. Association for Computational Linguistics, (2019)A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models., , , , , , , , and . WANLP@EACL, page 110-118. Association for Computational Linguistics, (2017)Deep Learning Models for Sentiment Analysis in Arabic., , , , , and . ANLP@ACL, page 9-17. Association for Computational Linguistics, (2015)