Relevance feature discovery for text mining
Li, Yuefeng, Algarni, Abdulmohsen, Albthan, Mubarak, Shen, Yan, & Bijaksana, Moch Arif (2014) Relevance feature discovery for text mining. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1656 -1669.
It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.
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|Item Type:||Journal Article|
|Keywords:||Text mining, Text feature extraction, Text classification|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 by IEEE|
|Deposited On:||15 Jan 2015 01:19|
|Last Modified:||20 May 2015 09:07|
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