Translation enhancement: a new relevance feedback method for cross-language information retrieval

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval (CLIR) settings. The studies of RF in CLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in CLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolve some out-of-vocabulary terms. In this paper, we propose a novel RF method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra translation alternatives if available so that the translated queries are more tuned to the current search. We studied TE using pseudo relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve CLIR with both types of RF methods, and that the improvement is comparable to that of QE. More importantly, the effects of TE and QE are complementary. Their integration can produce further improvement, and makes CLIR more robust for a variety of queries