Cross-Language Information Retrieval Using Multiple Resources and Combinations for Query Expansion

Abstract

As Internet resources become accessible to more and more countries, there is a need to develop efficient methods for information retrieval across languages. In the present paper, we focus on query expansion techniques to improve the effectiveness of an information retrieval. A combination to a dictionary-based translation and statistical-based disambiguation is indispensable to overcome translation’s ambiguity. We propose a model using multiple sources for query reformulation and expansion to select expansion terms and retrieve information needed by a user. Relevance feedback, thesaurus-based expansion, as well as a new feedback strategy, based on the extraction of domain keywords to expand user’s query, are introduced and evaluated. We tested the effectiveness of the proposed combined method, by an application to a French-English Information Retrieval. Experiments using CLEF data collection proved a great effectiveness of the proposed combined query expansion techniques.KeywordsInformation RetrievalAverage PrecisionRelevance FeedbackQuery TermQuery ExpansionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.