A two-stage approach for generating topic models

Xu, Yue, Gao, Yang, Li, Yuefeng, & Liu, Bin (2013) A two-stage approach for generating topic models. In Pei, Jian, Tseng, Vincent S., Cao, Longbing, Motoda, Hiroshi, & Xu, Guandong (Eds.) Lecture Notes in Computer Science : Advances in Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Gold Coast Convention and Exhibition Centre, Gold Coast, QLD, pp. 221-232.

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Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty.

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8 citations in Scopus
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ID Code: 60325
Item Type: Conference Paper
Refereed: Yes
Keywords: Topic modeling, Topic representation, Tf-idf, Frequent pattern mining, Entropy
DOI: 10.1007/978-3-642-37456-2_19
ISBN: 9783642374555
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200) > Analysis of Algorithms and Complexity (080201)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Conceptual Modelling (080603)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer-Verlag Berlin Heidelberg
Deposited On: 22 Jan 2014 23:20
Last Modified: 29 Jan 2014 06:04

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