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Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data.

Y. Wang, J. Kazama, Y. Tsuruoka, W. Chen, Y. Zhang, and K. Torisawa. IJCNLP, page 309-317. The Association for Computer Linguistics, (2011)

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TORISHIKI-KAI, An Autogenerated Web Search Directory.K. Torisawa, S. Saeger, Y. Kakizawa, J. Kazama, M. Murata, D. Noguchi, and A. Sumida. ISUC, page 179-186. IEEE Computer Society, (2008)The NICT concept dictionary.S. Saeger, K. Torisawa, J. Kazama, K. Ohtake, I. Varga, and Y. Yan. IUCS, page 403. IEEE, (2010)Large Scale Relation Acquisition Using Class Dependent Patterns.S. Saeger, K. Torisawa, J. Kazama, K. Kuroda, and M. Murata. ICDM, page 764-769. IEEE Computer Society, (2009)Speeding up Training with Tree Kernels for Node Relation Labeling.J. Kazama, and K. Torisawa. HLT/EMNLP, page 137-144. The Association for Computational Linguistics, (2005)Minimally Supervised Method for Multilingual Paraphrase Extraction from Definition Sentences on the Web.Y. Yan, C. Hashimoto, K. Torisawa, T. Kawai, J. Kazama, and S. Saeger. HLT-NAACL, page 63-73. The Association for Computational Linguistics, (2013)Looking for Trouble.S. Saeger, K. Torisawa, and J. Kazama. COLING, page 185-192. (2008)Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?Y. Tsuruoka, Y. Miyao, and J. Kazama. CoNLL, page 238-246. ACL, (2011)Large-Scale Verb Entailment Acquisition from the Web.C. Hashimoto, K. Torisawa, K. Kuroda, S. Saeger, M. Murata, and J. Kazama. EMNLP, page 1172-1181. ACL, (2009)Extracting Paraphrases from Definition Sentences on the Web.C. Hashimoto, K. Torisawa, S. Saeger, J. Kazama, and S. Kurohashi. ACL, page 1087-1097. The Association for Computer Linguistics, (2011)Inducing Gazetteers for Named Entity Recognition by Large-Scale Clustering of Dependency Relations.J. Kazama, and K. Torisawa. ACL, page 407-415. The Association for Computer Linguistics, (2008)

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Disambiguation of "Kazama, Jun'ichi"

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Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data.

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Other publications of authors with the same name

Disambiguation

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URL
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APA
Chicago
DIN 1505
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Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data.