A Comparative Study for Query Translation using Linear Combination and Confidence Measure

In Cross Language Information Retrieval (CLIR), query terms can be translated to the document language using Bilingual Dictionaries (BDs) or Statistical Transla-tion Models (STMs). Combining different translation resources can also be used to improve the performance. Unfortunately, the most studies on combining multiple re-sources use simple methods such as linear combination. In this paper, we drew up a comparative study between linear combina-tion and confidence measures to combine multiple translation resources for the pur-pose of CLIR. We show that the linear combination method is unable to combine correctly different types of resources such as BDs and STMs. While the confidence measure method is able to re-weight the translation candidate more radically than in linear combination. It reconsiders each translation candidate proposed by different resources with respect to additional fea-tures. We tested the two methods on differ-ent test CLIR collections and the results show that the confidence measure outper-forms the linear combination method.