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 Translation Models (STMs). Combining different translation resources can also be used to improve the performance. Unfortunately, the most studies on combining multiple resources use simple methods such as linear combination. In this paper, we drew up a comparative study between linear combination and confidence measures to combine multiple translation resources for the purpose 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 features. We tested the two methods on different test CLIR collections and the results show that the confidence measure outperforms the linear combination method.