Query translation is an important task in cross-language information retrieval (CLIR) aiming to translate queries into languages used in documents. The purpose of this paper is to investigate the necessity of translating query terms, which might differ from one term to another. Some untranslated terms cause irreparable performance drop while others do not. We propose an approach to estimate the translation probability of a query term, which helps decide if it should be translated or not. The approach learns regression and classification models based on a rich set of linguistic and statistical properties of the term. Experiments on NTCIR-4 and NTCIR-5 English-Chinese CLIR tasks demonstrate that the proposed approach can significantly improve CLIR performance. An in-depth analysis is also provided for discussing the impact of untranslated out-of-vocabulary (OOV) query terms and translation quality of non-OOV query terms on CLIR performance.

Formulating appropriate and effective queries has been regarded as a challenging issue, since a large number of candidate words or phrases could be chosen as query terms to convey users' information needs. In this paper, we propose an approach to rank a set of given query terms according their effectiveness, wherein top ranked terms will be selected as an effective query. Our ranking approach exploits and benefits from the underlying relationship between the query terms, and thereby the effective terms can be properly combined into the query. Two regression models which capture a rich set of linguistic and statistical properties are used in our approach. Experiments on NTCIR-4 ad-hoc retrieval tasks demonstrate that the proposed approach can significantly improve retrieval performance, and can be well applied to other problems such as query expansion and querying by text segments.