Details

Autor(en) / Beteiligte

• Gao, Jianfeng
• Nie, Jian-Yun
• Xun, Endong
• Zhang, Jian
• Zhou, Ming
• Huang, Changning

Titel

Improving query translation for cross-language information retrieval using statistical models

Ist Teil von

• Annual ACM Conference on Research and Development in Information Retrieval: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, p.96-104

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2001

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• Dictionaries have often been used for query translation in cross-language information retrieval (CLIR). However, we are faced with the problem of translation ambiguity, i.e. multiple translations are stored in a dictionary for a word. In addition, a word-by-word query translation is not precise enough. In this paper, we explore several methods to improve the previous dictionary-based query translation. First, as many as possible, noun phrases are recognized and translated as a whole by using statistical models and phrase translation patterns. Second, the best word translations are selected based on the cohesion of the translation words. Our experimental results on TREC English-Chinese CLIR collection show that these techniques result in significant improvements over the simple dictionary approaches, and achieve even better performance than a high-quality machine translation system.