Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis

Daisuke Kawahara; Yuta Hayashibe; Hajime Morita; Sadao Kurohashi

Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis

Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, Sadao Kurohashi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.

Original language	English
Title of host publication	IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings
Publisher	Association for Computational Linguistics (ACL)
Pages	1-10
Number of pages	10
ISBN (Electronic)	9781945626739
Publication status	Published - 2017
Externally published	Yes
Event	15th International Conference on Parsing Technologies, IWPT 2017 - Pisa, Italy Duration: 2017 Sept 20 → 2017 Sept 22

Publication series

Name	IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings

Conference

Conference	15th International Conference on Parsing Technologies, IWPT 2017
Country/Territory	Italy
City	Pisa
Period	17/9/20 → 17/9/22

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

Cite this

Kawahara, D., Hayashibe, Y., Morita, H., & Kurohashi, S. (2017). Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis. In IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings (pp. 1-10). (IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings). Association for Computational Linguistics (ACL).

Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis. / Kawahara, Daisuke; Hayashibe, Yuta; Morita, Hajime et al.
IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings. Association for Computational Linguistics (ACL), 2017. p. 1-10 (IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kawahara, D, Hayashibe, Y, Morita, H & Kurohashi, S 2017, Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis. in IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings. IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings, Association for Computational Linguistics (ACL), pp. 1-10, 15th International Conference on Parsing Technologies, IWPT 2017, Pisa, Italy, 17/9/20.

Kawahara D, Hayashibe Y, Morita H, Kurohashi S. Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis. In IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings. Association for Computational Linguistics (ACL). 2017. p. 1-10. (IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings).

Kawahara, Daisuke ; Hayashibe, Yuta ; Morita, Hajime et al. / Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis. IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings. Association for Computational Linguistics (ACL), 2017. pp. 1-10 (IWPT 2017 - 15th International Conference on Parsing Technologies, Proceedings).

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abstract = "This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.",

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Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis

Abstract

Publication series

Conference

ASJC Scopus subject areas

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