Academic & Scientific Articles
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Item New Technique to Deal With Verbose Queries in Social Book Search(CERIST, 2017) Chaa, Messaoud; Nouali, Omar; Bellot, PatriceVerbose query reduction and query term weighting are automatic techniques to deal with verbose queries. The objective is either to assign an appropriate weight to query terms according to their importance in the topic, or outright remove unsuitable terms from the query and keep only the suitable terms to the topic and user’s need. These techniques improve performance and provide good results for ad hoc information retrieval. In this paper we propose a new approach to deal with long verbose queries in Social Information Re-trieval (SIR) by taking Social Book Search as an example. In this approach, a new statistical measure was introduced to reduce and weight terms of verbose queries. Next, we expand he query by exploiting the similar books mentioned by users in their queries. We find that the proposed approach improves significantly the results.Item Verbose Query Reduction by Learning to Rank for Social Book Search Track(CERIST, 2016-07) Chaa, Messaoud; Nouali, Omar; Bellot, PatriceIn this paper, we describe our participation in the INEX 2016 Social Book Search Suggestion Track (SBS). We have exploited machine learning techniques to rank query terms and assign an appropriate weight to each one before applying a probabilistic information retrieval model (BM15). Thereafter, only the top-k terms are used in the matching model. Several features are used to describe each term, such as statistical features, syntactic features and others features like whether the term is present in similar books and in the profile of the topic starter. The model was learned using the 2014 and 2015 topics and tested with the 2016 topics. Our experiments show that our approach improves the search results.