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Topic extraction from text documents using multiple-cause networks

Title
Topic extraction from text documents using multiple-cause networks
Authors
Chang J.-H.Lee J.W.Kim Y.Zhang B.-T.
Ewha Authors
김유섭
Issue Date
2002
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
vol. 2417, pp. 434 - 443
Publisher
Springer Verlag
Indexed
SCOPUS scopus
Abstract
This paper presents an approach to the topic extraction from text documents using probabilistic graphical models. Multiple-cause networks with latent variables are used and the Helmholtz machines are utilized to ease the learning and inference. The learning in this model is conducted in a purely data-driven way and does not require prespecified categories of the given documents. Topic words extraction experiments on the TDT-2collection are presented. Especially, document clustering results on a subset of TREC-8 ad-hoc task data show the substantial reduction of the inference time without significant deterioration of performance. © Springer-Verlag Berlin Heidelberg 2002.
ISBN
3540440380; 9783540440383
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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