Statistical Dependency Analysis with Support Vector Machines

Hiroyasu Yamada, Yuji Matsumoto


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W03-3023.pdf