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Weakly Supervised Part-of-Speech Tagging for Morphologically-Rich, Resource-Scarce Languages., and . EACL, page 363-371. The Association for Computer Linguistics, (2009)Predicting Stance in Ideological Debate with Rich Linguistic Knowledge., and . COLING (Posters), page 451-460. Indian Institute of Technology Bombay, (2012)Extra-Linguistic Constraints on Stance Recognition in Ideological Debates., and . ACL (2), page 816-821. The Association for Computer Linguistics, (2013)Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates., and . EMNLP, page 751-762. ACL, (2014)Frame Semantics for Stance Classification., and . CoNLL, page 124-132. ACL, (2013)Learning-Based Named Entity Recognition for Morphologically-Rich, Resource-Scarce Languages., , and . EACL, page 354-362. The Association for Computer Linguistics, (2009)Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art., and . COLING (Posters), page 365-373. Chinese Information Processing Society of China, (2010)End-to-End Reading Comprehension with Dynamic Answer Chunk Ranking., , , , , and . CoRR, (2016)Slot-Filling by Substring Extraction at TAC KBP 2012 (Team Papelo)., , and . TAC, NIST, (2012)Stance Classification of Ideological Debates: Data, Models, Features, and Constraints., and . IJCNLP, page 1348-1356. Asian Federation of Natural Language Processing / ACL, (2013)