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Query expansion in information retrieval : What can we learn from a deep analysis of queries ?

Ermakova, Liana and Mothe, Josiane and Ovchinnikova, Irina Query expansion in information retrieval : What can we learn from a deep analysis of queries ? (2014) In: International Conference on Computational Linguistics - Dialogue 2014, 4 June 2014 - 8 June 2014 (Moscow, Russian Federation).

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Abstract

Information retrieval aims at retrieving relevant documents answering a user's need expressed through a query. Users' queries are generally less than 3 words which make challenging to answer correctly. Automatic query expansion (QE) improves the precision in average even if it can decrease the results for some queries. In this paper, we propose a new automatic QE method that estimates the importance of expansion candidate terms by the strength of their relation to the query terms. The method combines local analysis and global analysis of texts. We evaluate the method using international benchmark collections and measures. We found comparable results in average compared to the Bo2 method. However, we show that a deep analysis of initial and expanded queries brings interesting insights that could help for future research in the domain.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-01142602
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:30 Mar 2015 07:44

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