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CIKM
2009
Springer

Topic and keyword re-ranking for LDA-based topic modeling

13 years 9 months ago
Topic and keyword re-ranking for LDA-based topic modeling
Topic-based text summaries promise to help average users quickly understand a text collection and derive insights. Recent research has shown that the Latent Dirichlet Allocation (LDA) model is one of the most effective approaches to topic analysis. However, the LDA-based results may not be ideal for human understanding and consumption. In this paper, we present several topic and keyword re-ranking approaches that can help users better understand and consume the LDA-derived topics in their text analysis. Our methods process the LDA output based on a set of criteria that model a user’s information needs. Our evaluation demonstrates the usefulness of the methods in summarizing several large-scale, real world data sets. Categories and Subject Descriptors: I.2.6 [Artificial Intelligence]: Learning H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing General Terms: Algorithms, Experimentation
Yangqiu Song, Shimei Pan, Shixia Liu, Michelle X.
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Yangqiu Song, Shimei Pan, Shixia Liu, Michelle X. Zhou, Weihong Qian
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