Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis

Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, Sadao Kurohashi


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.
Anthology ID:
W17-6301
Volume:
Proceedings of the 15th International Conference on Parsing Technologies
Month:
September
Year:
2017
Address:
Pisa, Italy
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/W17-6301
DOI:
Bibkey:
Cite (ACL):
Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, and Sadao Kurohashi. 2017. Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis. In Proceedings of the 15th International Conference on Parsing Technologies, pages 1–10, Pisa, Italy. Association for Computational Linguistics.
Cite (Informal):
Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis (Kawahara et al., 2017)
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PDF:
https://aclanthology.org/W17-6301.pdf