Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Resolving ambiguity for cross-language retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Improving query translation for cross-language information retrieval using statistical models
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Comparing cross-language query expansion techniques by degrading translation resources
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Statistical cross-language information retrieval using n-best query translations
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An algorithm for finding noun phrase correspondences in bilingual corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Translating unknown queries with web corpora for cross-language information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Is a query worth translating: ask the users!
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Translation techniques in cross-language information retrieval
ACM Computing Surveys (CSUR)
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Query translation is an important task in cross-language information retrieval (CLIR) aiming to translate queries into languages used in documents. The purpose of this paper is to investigate the necessity of translating query terms, which might differ from one term to another. Some untranslated terms cause irreparable performance drop while others do not. We propose an approach to estimate the translation probability of a query term, which helps decide if it should be translated or not. The approach learns regression and classification models based on a rich set of linguistic and statistical properties of the term. Experiments on NTCIR-4 and NTCIR-5 English-Chinese CLIR tasks demonstrate that the proposed approach can significantly improve CLIR performance. An in-depth analysis is also provided for discussing the impact of untranslated out-of-vocabulary (OOV) query terms and translation quality of non-OOV query terms on CLIR performance.