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
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Note:
Pages:
195–206
Language:
URL:
https://www.aclweb.org/anthology/W03-3023
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W03-3023.pdf