Research on query translation for clir based on a combination of statistical method and web information

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 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 which integrates the traits of the three models, and it gradually filters and optimizes the translation. This paper also gives an approach to resolve the translation problem of the words which are out of vocabulary (OOV), which uses web feedback information to get translation and expansion of query. 1553-9105/