Using co-occurrence tendencies to improve Cross-Language Information Retrieval

Query disambiguation is considered as one of the most important methods in improving the effectiveness of information retrieval. In the present paper, we focus on query terms disambiguation via, a combined statistical method both before and after translation, in order to avoid source language ambiguity as well as incorrect selection of target translations. By combining query expansion with dictionary-based translation and statistics-based disambiguation, in order to overcome query terms ambiguity, information retrieval should become much more efficient. Thus, query expansion techniques through relevance feedback were performed prior to either the first or the second disambiguation processes. We tested the effectiveness of the proposed combined method, by an application to a French-English Information Retrieval. Experiments involving TREC data collection revealed the proposed disambiguation and expansion methods to be highly effective