Paper: A Deterministic Word Dependency Analyzer Enhanced With Preference Learning

ACL ID C04-1040
Title A Deterministic Word Dependency Analyzer Enhanced With Preference Learning
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

Word dependency is important in parsing tech- nology. Some applications such as Informa- tion Extraction from biological documents ben- efit from word dependency analysis even with- out phrase labels. Therefore, we expect an ac- curate dependency analyzer trainable without using phrase labels is useful. Although such an English word dependency analyzer was pro- posed by Yamada and Matsumoto, its accu- racy is lower than state-of-the-art phrase struc- ture parsers because of the lack of top-down in- formation given by phrase labels. This paper shows that the dependency analyzer can be im- proved by introducing a Root-Node Finder and a Prepositional-Phrase Attachment Resolver. Experimental results show that these modules based on Preference Learning give better scores than Collins’ Model 3...