Exploiting poly-lingual documents for improving text categorization effectiveness

  • Authors:

  • Chih-Ping Wei;Chin-Sheng Yang;Ching-Hsien Lee;Huihua Shi;Christopher C. Yang

  • Affiliations:

  • Department of Information Management, National Taiwan University, Taipei, Taiwan, ROC;Department of Information Management, Yuan Ze University, Chung-Li, Taiwan, ROC;Department of Information Management, National Taiwan University, Taipei, Taiwan, ROC;Infrastructure & System Department I, Information Technology Division (AUT), AU Optronics Corporation, Hsinchu Science Park, Hsinchu, Taiwan, ROC;College of Computing and Informatics, Drexel University, Philadelphia, PA, USA

  • Venue:
  • Decision Support Systems
  • Year:
  • 2014

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Abstract

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 naive 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.