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2005
ACM

Discovering evolutionary theme patterns from text: an exploration of temporal text mining

10 years 8 months ago
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task ? discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Clustering General Terms: Algorithms
Qiaozhu Mei, ChengXiang Zhai
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Qiaozhu Mei, ChengXiang Zhai
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