Abstract

Query translation is an important task for cross-language information retrieval (CLIR), which aims at translating the query described in source language into target language. The approach to query translation based on bilingual dictionary is becoming the mainstream thinking because of its simplicity and the increasing availability of machine readable bilingual dictionary. However, this kind of approach faces two necessary problems that is ambiguity in translation and the incompleteness of the dictionary. This paper focuses on the first problem, and it presents three statistical models based on HowNet to resolve query translation ambiguity of CLIR: query translation selection based on semantic relation; bilingual decaying co-occurrence model and semantic decaying co-occurrence model. Through test and summarizing this paper gets the best algorithm to integrate the traits of the three models, which gradually filters and optimizes the translation.

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