Multilingual Web retrieval: An experiment in English–Chinese business intelligence

  • Authors:

  • Jialun Qin;Yilu Zhou;Michael Chau;Hsinchun Chen

  • Affiliations:

  • Department of Management Information Systems, The University of Arizona, Tucson, AZ 85721;Department of Management Information Systems, The University of Arizona, Tucson, AZ 85721;School of Business, The University of Hong Kong, Hong Kong, People's Republic of China;Department of Management Information Systems, The University of Arizona, Tucson, AZ 85721

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2006

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Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIR), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English–Chinese Web portal that incorporates various CLIR techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-by-word translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise. © 2006 Wiley Periodicals, Inc.