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Improving Feature-Rich Transition-Based Constituent Parsing Using Recurrent Neural Networks.

C. Ma, A. Tamura, L. Liu, T. Zhao, and E. Sumita. IEICE Transactions, 100-D (9): 2205-2214 (2017)

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Cross-lingual Text Classification Using Topic-Dependent Word Probabilities.D. Andrade, K. Sadamasa, A. Tamura, and M. Tsuchida. HLT-NAACL, page 1466-1471. The Association for Computational Linguistics, (2015)Unsupervised Word Alignment by Agreement Under ITG Constraint.H. Kamigaito, A. Tamura, H. Takamura, M. Okumura, and E. Sumita. EMNLP, page 1998-2004. The Association for Computational Linguistics, (2016)Japanese Dependency Analysis Using the Ancestor-Descendant Relation.A. Tamura, H. Takamura, and M. Okumura. EMNLP-CoNLL, page 600-609. ACL, (2007)Neural Machine Translation Incorporating Named Entity.A. Ugawa, A. Tamura, T. Ninomiya, H. Takamura, and M. Okumura. COLING, page 3240-3250. Association for Computational Linguistics, (2018)Classification of Multiple-Sentence Questions.A. Tamura, H. Takamura, and M. Okumura. IJCNLP, volume 3651 of Lecture Notes in Computer Science, page 426-437. Springer, (2005)Extractive Summarization Method for Contact Center Dialogues based on Call Logs.A. Tamura, K. Ishikawa, M. Saikou, and M. Tsuchida. IJCNLP, page 500-508. The Association for Computer Linguistics, (2011)Distortion Model Based on Word Sequence Labeling for Statistical Machine Translation.I. Goto, M. Utiyama, E. Sumita, A. Tamura, and S. Kurohashi. ACM Trans. Asian Lang. Inf. Process., 13 (1): 2:1-2:21 (2014)Improving Feature-Rich Transition-Based Constituent Parsing Using Recurrent Neural Networks.C. Ma, A. Tamura, L. Liu, T. Zhao, and E. Sumita. IEICE Transactions, 100-D (9): 2205-2214 (2017)Neural Machine Translation with Source Dependency Representation.K. Chen, R. Wang, M. Utiyama, L. Liu, A. Tamura, E. Sumita, and T. Zhao. EMNLP, page 2846-2852. Association for Computational Linguistics, (2017)Forest-Based Neural Machine Translation.C. Ma, A. Tamura, M. Utiyama, T. Zhao, and E. Sumita. ACL (1), page 1253-1263. Association for Computational Linguistics, (2018)

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Improving Feature-Rich Transition-Based Constituent Parsing Using Recurrent Neural Networks.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving Feature-Rich Transition-Based Constituent Parsing Using Recurrent Neural Networks.

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Improving Feature-Rich Transition-Based Constituent Parsing Using Recurrent Neural Networks.