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Exploiting poly-lingual documents for improving text categorization effectiveness
Journal article   Peer reviewed

Exploiting poly-lingual documents for improving text categorization effectiveness

Chih-Ping Wei, Chin-Sheng Yang, Ching-Hsien Lee, Huihua Shi and Christopher C. Yang
Decision Support Systems, v 57(1), pp 64-76
Jan 2014
DOI: https://doi.org/10.1016/j.dss.2013.08.001

Abstract

Document management Feature reinforcement Poly-lingual text categorization Text categorization Text mining
With the globalization of business environments and rapid emergence and proliferation of the Internet, organizations or individuals often generate, acquire, and then archive documents written in different languages (i.e., poly-lingual documents). Prevalent document management practice is to use categories to organize this ever-increasing volume of poly-lingual documents for subsequent searches and accesses. Poly-lingual text categorization (PLTC) refers to the automatic learning of text categorization models from a set of preclassified training documents written in different languages and the subsequent assignment of unclassified poly-lingual documents to predefined categories on the basis of the induced text categorization models. Although PLTC can be approached as multiple, independent monolingual text categorization problems, this naïve PLTC approach employs only the training documents of the same language to construct a monolingual classifier and thus fails to exploit the opportunity offered by poly-lingual training documents. In this study, we propose a feature-reinforcement-based PLTC (FR-PLTC) technique that takes into account the training documents of all languages when constructing a monolingual classifier for a specific language. Using the independent monolingual text categorization (MnTC) approach as a performance benchmark, the empirical evaluation results show that our proposed FR-PLTC technique achieves higher classification accuracy than the benchmark technique. In addition, our empirical results suggest the superiority of the proposed FR-PLTC technique over its counterpart across a range of training sizes. •We identify the problem of poly-lingual text categorization (PLTC).•We propose a feature-reinforcement-based PLTC (FR-PLTC) technique.•Our FR-PLTC technique considers the training documents of all languages.•Our FR-PLTC technique outperforms the benchmark technique.

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8 citations in Web of Science
7 citations in Scopus

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Industry collaboration
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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Operations Research & Management Science
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