Study of cross lingual information retrieval using on-line translation systems

Authors:
Rong Jin;Joyce Y. Chai
Affiliations:
Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI
Venue:
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2005

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Abstract

Typical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and parallel corpus. In this study, we focus on the cross lingual retrieval problem that only uses online translation systems. We compare two approaches: a translation-based approach that directly translates queries into the language of documents and then applies traditional information retrieval techniques; and a model-based approach that first learns a statistical translation model from the translations acquired from an online translation system and then applies the learned statistical model to cross lingual information retrieval. Our empirical study with ImageCLEF has shown the model-based approach performs significantly better than the translation-based approach.