Proceedings

Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1915-1919.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-320

Abstract

To support more precise query translation for English-Chinese Bi-Directional Cross-Language Information Retrieval (CLIR), we have developed a novel framework by integrating a semantic network to characterize the correlations between multiple inter-related text terms of interest and learn their inter-related statistical query translation models. First, a semantic network is automatically generated from large-scale English-Chinese bilingual parallel corpora to characterize the correlations between a large number of text terms of interest. Second, the semantic network is exploited to learn the statistical query translation models for such text terms of interest. Finally, these inter-related query translation models are used to translate the queries more precisely and achieve more effective CLIR. Our experiments on a large number of official public data have obtained very positive results.

Yuejie Zhang, Lei Cen, Cheng Jin, Xiangyang Xue, and Jianping Fan. 2011. Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1915–1919. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-320
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