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Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining.

E. Shnarch, C. Alzate, L. Dankin, M. Gleize, Y. Hou, L. Choshen, R. Aharonov, and N. Slonim. ACL (2), page 599-605. Association for Computational Linguistics, (2018)

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Addressing Discourse and Document Structure in the RTE Search Task.S. Mirkin, R. Bar-Haim, I. Dagan, E. Shnarch, A. Stern, I. Szpektor, and J. Berant. TAC, NIST, (2009)Instance-based Evaluation of Entailment Rule Acquisition.I. Szpektor, E. Shnarch, and I. Dagan. ACL, The Association for Computational Linguistics, (2007)Recognising Entailment within Discourse.S. Mirkin, J. Berant, I. Dagan, and E. Shnarch. COLING, page 770-778. Tsinghua University Press, (2010)Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining.E. Shnarch, C. Alzate, L. Dankin, M. Gleize, Y. Hou, L. Choshen, R. Aharonov, and N. Slonim. ACL (2), page 599-605. Association for Computational Linguistics, (2018)Text Categorization from Category Name via Lexical Reference.L. Barak, I. Dagan, and E. Shnarch. HLT-NAACL (Short Papers), page 33-36. The Association for Computational Linguistics, (2009)Knowledge and Tree-Edits in Learnable Entailment Proofs.A. Stern, S. Mirkin, E. Shnarch, L. Kotlerman, I. Dagan, A. Lotan, and J. Berant. TAC, NIST, (2011)Towards a Probabilistic Model for Lexical Entailment.E. Shnarch, J. Goldberger, and I. Dagan. TextInfer@EMNLP, page 10-19. Association for Computational Linguistics, (2011)Semantic Relatedness of Wikipedia Concepts - Benchmark Data and a Working Solution.L. Ein-Dor, A. Halfon, Y. Kantor, R. Levy, Y. Mass, R. Rinott, E. Shnarch, and N. Slonim. LREC, European Language Resources Association (ELRA), (2018)Semantic Inference at the Lexical-Syntactic Level.R. Bar-Haim, I. Dagan, I. Greental, and E. Shnarch. AAAI, page 871-876. AAAI Press, (2007)Efficient Semantic Deduction and Approximate Matching over Compact Parse Forests.R. Bar-Haim, I. Dagan, S. Mirkin, E. Shnarch, I. Szpektor, J. Berant, and I. Greental. TAC, NIST, (2008)

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Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining.

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Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining.