TCtract-A Collocation Extraction Approach for Noun Phrases Using Shallow Parsing Rules and Statistic Models

PACLIC 20 / Wuhan, China / 1-3 November, 200

Learning Head-modifier Pairs to Improve Lexicalized Dependency Parsing on a Chinese Treebank

Proceedings of the Sixth International Workshop on Treebanks and Linguistic Theories. Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler. NEALT Proceedings Series, Vol. 1 (2007), 201-212. © 2007 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/4476

A hybrid extraction model for Chinese noun/verb synonym bi-gram

2011-2012 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Learning Verb-Noun Relations to Improve Parsing

The verb-noun sequence in Chinese often creates ambiguities in parsing. These ambiguities can usually be resolved if we know in advance whether the verb and the noun tend to be in the verb-object relation or the modifier-head relation. In this paper, we describe a learning procedure whereby such knowledge can be automatically acquired. Using an existing (imperfect) parser with a chart filter and a tree filter, a large corpus, and the log-likelihood-ratio (LLR) algorithm, we were able to acquire verb-noun pairs which typically occur either in verbobject relations or modifier-head relations. The learned pairs are then used in the parsing process for disambiguation. Evaluation shows that the accuracy of the original parser improves significantly with the use of the automatically acquired knowledge.

Learning Verb-Noun Relations to Improve Parsing

The verb-noun sequence in Chinese often creates ambiguities in parsing. These ambiguities can usually be resolved if we know in advance whether the verb and the noun tend to be in the verb-object relation or the modifier-head relation. In this paper, we describe a learning procedure whereby such knowledge can be automatically acquired. Using an existing (imperfect) parser with a chart filter and a tree filter, a large corpus, and the log-likelihood-ratio (LLR) algorithm, we were able to acquire verb-noun pairs which typically occur either in verbobject relations or modifier-head relations. The learned pairs are then used in the parsing process for disambiguation. Evaluation shows that the accuracy of the original parser improves significantly with the use of the automatically acquired knowledge.