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Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models.

, , and . HLT-NAACL, page 683-693. The Association for Computational Linguistics, (2015)

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A Simple, Fast, and Effective Reparameterization of IBM Model 2., , and . HLT-NAACL, page 644-648. The Association for Computational Linguistics, (2013)Supersense Tagging for Arabic: the MT-in-the-Middle Attack., , , , and . HLT-NAACL, page 661-667. The Association for Computational Linguistics, (2013)Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models., , and . HLT-NAACL, page 683-693. The Association for Computational Linguistics, (2015)Unsupervised POS Induction with Word Embeddings., , , and . HLT-NAACL, page 1311-1316. The Association for Computational Linguistics, (2015)Unsupervised Recurrent Neural Network Grammars., , , , , and . NAACL-HLT (1), page 1105-1117. Association for Computational Linguistics, (2019)Incorporating Structural Alignment Biases into an Attentional Neural Translation Model., , , , , and . HLT-NAACL, page 876-885. The Association for Computational Linguistics, (2016)What Do Recurrent Neural Network Grammars Learn About Syntax?, , , , , and . EACL (1), page 1249-1258. Association for Computational Linguistics, (2017)Identifying the L1 of non-native writers: the CMU-Haifa system., , , , , , , and . BEA@NAACL-HLT, page 279-287. The Association for Computer Linguistics, (2013)978-1-937284-47-3.The CMU Submission for the Shared Task on Language Identification in Code-Switched Data., , , and . CodeSwitch@EMNLP, page 80-86. Association for Computational Linguistics, (2014)A Supertag-Context Model for Weakly-Supervised CCG Parser Learning., , , and . CoNLL, page 22-31. ACL, (2015)