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Cited 23 time in webofscience Cited 23 time in scopus
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Dependency structure language model for topic detection and tracking SCIE SCOPUS

Title
Dependency structure language model for topic detection and tracking
Authors
Lee, CLee, GGJang, M
Date Issued
2007-09
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Abstract
In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches. (c) 2006 Published by Elsevier Ltd.
Keywords
dependency structure language model; term dependence; dependency parse tree; topic detection and tracking
URI
https://oasis.postech.ac.kr/handle/2014.oak/23363
DOI
10.1016/j.ipm.2006.02.007
ISSN
0306-4573
Article Type
Article
Citation
INFORMATION PROCESSING & MANAGEMENT (postech rank 1), vol. 43, no. 5, page. 1249 - 1259, 2007-09
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