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Bootstrapping Lexical Choice via Multiple-Sequence Alignment
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Automatic Topic Detection with an Incremental Clustering Algorithm

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At present, most of the topic detection approaches are not accurate and efficient enough. In this paper, we proposed a new topic detection method (TPIC) based on an incremental clustering algorithm. It employs a self-refinement process of discriminative feature identification and a term reweighting algorithm to accurately cluster the given documents which discuss the same topic. To be efficient, the “aging” nature of topics is used to precluster stories. To automatically detect the true number of topics, Bayesian Information Criterion (BIC) is used to estimate the true number of topics. Experimental results on Linguistic Data Consortium (LDC) datasets TDT4 show ...
Mentions: China Beihang University BIC