SPEEDSER: A Possibilistic System for Query Disambiguation, Expansion and Translation

We design, implement and assess in this paper a new architecture of a possibilistic mono- and cross-language information retrieval (IR/CLIR) system. The latter is useful to experiment query disambiguation, expansion and translation processes in both IR and CLIR frameworks. We take advantage of possibility theory to overcome the problems of query disambiguation and expansion in an uncertain and imprecise IR/CLIR context. We investigate the impact of combining possibilistic query disambiguation with expansion on IR/CLIR efficiency. A co-occurrence graph representation is exploited to quantify the similarity between query terms and their semantically close words (expansion task) or between query terms and their possible meanings (disambiguation task). We extend the possibilistic mono-language query disambiguation approach to a cross-language framework. We conduct a set of experiments using ROMANSEVAL data collection, the French–English parallel text corpus Europarl and the CLEF-2003 French–English IR/CLIR test collection. Results highlight some statistically significant improvements of our possibilistic approaches when compared to some state-of-the-art IR/CLIR works.