Abstract
Query disambiguation is considered as one of the most important methods in improving the effectiveness of information retrieval. 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. 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. 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.
Highlights
Query disambiguation is considered as one of the most important methods in improving the effectiveness of information retrieval
We conducted two types of experiments. Those related to the query translation/disambiguation and those related to the query expansion before and/or after translation
We tested and evaluated two methods fulfilling the disambiguation of translated queries and the organization of source queries, using the co-occurrence tendency and the following estimations: Log-Likelihood Ratio (LLR) and Mutual Information (MI)
Summary
Query disambiguation is considered as one of the most important methods in improving the effectiveness of information retrieval. 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. 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. We includes a statistical approach for a focus on query translation, disambiguation can significantly reduce disambiguation and expansion in order to improve the effectiveness of information errors associated with polysemy in dictionary translation. As an assumption to reduce the effect of ambiguity and errors that a dictionarybased method would cause, a combined statistical disambiguation method is performed both prior to and after translation.
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