Abstract
In this paper, we propose a method for analyzing word-word dependencies using deterministic bottom-up manner using Support Vector machines. We experimented with dependency trees converted from Penn treebank data, and achieved over 90% accuracy of word-word dependency. Though the result is little worse than the most up-to-date phrase structure based parsers, it looks satisfactorily accurate considering that our parser uses no information from phrase structures.- Anthology ID:
- W03-3023
- Volume:
- Proceedings of the Eighth International Conference on Parsing Technologies
- Month:
- April
- Year:
- 2003
- Address:
- Nancy, France
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Note:
- Pages:
- 195–206
- Language:
- URL:
- https://aclanthology.org/W03-3023
- DOI:
- Cite (ACL):
- Hiroyasu Yamada and Yuji Matsumoto. 2003. Statistical Dependency Analysis with Support Vector Machines. In Proceedings of the Eighth International Conference on Parsing Technologies, pages 195–206, Nancy, France.
- Cite (Informal):
- Statistical Dependency Analysis with Support Vector Machines (Yamada & Matsumoto, IWPT 2003)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W03-3023.pdf