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
- Editors:
- Yusuke Miyao, Kenji Sagae
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/W17-6301
- DOI:
- 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., IWPT 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/W17-6301.pdf