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Dependency structure language model for topic detection and tracking
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DOI：10.1016/j.ipm.2006.02.007.png)
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Abstract
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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
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