Pattern-based topic models for information filtering

Gao, Yang, Xu, Yue, & Li, Yuefeng (2013) Pattern-based topic models for information filtering. In Cambria, Erik & Chen, Ping (Eds.) Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE), 7 December 2013, Dallas, Texas.


Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

Impact and interest:

6 citations in Scopus
3 citations in Web of Science®
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ID Code: 66686
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Topic modeling, Pattern mining, Information filtering, User interest
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Please consult the authors
Deposited On: 12 Feb 2014 05:14
Last Modified: 22 Jun 2017 18:22

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