Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5202
PIRA download icon_1.1View/Download Full Text
Title: A hybrid extraction model for Chinese noun/verb synonym bi-gram
Authors: Li, W
Lu, Q 
Issue Date: 16-Dec-2011
Source: Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation (PACLIC 25), 16-18 Dec, Nanyang Technological University, Singapore, p. 430-439
Abstract: Statistical-based collocation extraction approaches suffer from (1) low precision rate because high co-occurrence bi-grams may be syntactically unrelated and are thus not true collocations; (2) low recall rate because some true collocations with low occurrences cannot be identified successfully by statistical-based models. To integrate both syntactic rules as well as semantic knowledge into a statistical model for collocation extraction is one way to achieve a high precision while keeping a reasonable recall. This paper designs a cascade system which employs a hybrid model by integrating both syntactic and semantic knowledge into a statistical model for Chinese synonymous noun/verb collocations extraction. The grammatically bounded noun/verb collocations are extracted first from a syntactic-rule based module, which is then inputted to a semantic-based module for further retrieval of low frequent bi-gram collocations.
Keywords: Collocation extraction
Statistical model
Syntactic rules
Semantic relationship
Similarity calculation
HowNet
Publisher: Institute for Digital Enhancement of Cognitive Development, Waseda University
ISBN: 978-4-905166-02-3
Rights: © 2011 The PACLIC 25 Organizing Committee and PACLIC Steering Committee
Copyright of contributed papers reserved by respective authors
Copyright 2011 by Wanyin Li, Qin Lu
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Li_Hybrid_Extraction_Bi-gram.pdf135.34 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

122
Last Week
1
Last month
Citations as of Apr 14, 2024

Downloads

119
Citations as of Apr 14, 2024

SCOPUSTM   
Citations

1
Last Week
0
Last month
Citations as of Apr 19, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.