A Combination of Reduction and Expansion Approaches to Deal with Long Natural Language queries

Abstract

Most of the queries submitted to search engines are composed of keywords but it is not enough for users to express their needs. Through verbose natural language queries, users can express complex or highly specific information needs. However, it is difficult for search engine to deal with this type of queries. Moreover, the emergence of social medias allows users to get opinions, suggestions or recommendations from other users about complex information needs. In order to increase the understandability of user needs, tasks as the CLEF Social Book Search Suggestion Track have been proposed from 2011 to 2016. The aim is to investigate techniques to support users in searching for books in catalogs of professional metadata and complementary social media. In this context, we introduce in the current paper a statical approach to deal with long verbose queries in Social Information Retrieval (SIR) by taking Social Book Search(SBS) as a study case. firstly, a morphosyntactic analysis was introduced to reduce verbose queries, the second step is based on expanding the reduced queries using association rules mining combined with Pseudo relevance feedback. Experiments on SBS 2014 and 2016 collections show significant improvement in the retrieval performance.