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Dependency Structure Language Model for Topic Detection and Tracking
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- Authors
- Chang Ki Lee, Gary Geun Bae Lee, Myung Gil Jang
- Issue Date
- 2007-09
- Citation
- Information Processing & Management, v.43, no.5, pp.1249-1259
- ISSN
- 0306-4573
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- 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. © 2006.
- KSP Keywords
- Dependency Parse, Dependency Structure, Language Model, Long distance, Parse tree, Proposed model, Topic tracking, topic detection and tracking