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Modèle de compréhension du besoin en information pour la RI conversationnelle

Abstract : IR is based on a standard framework that queries document collections through an information need expressed as a set of keywords. Our contribution aims at overpassing this usual paradigm by starting the retrieval process from the natural language expression of the information need; giving rise to a new generation of IR systems based on conversational features (also called "search-oriented conversational systems"). Therefore, the first step focuses on the query formulation from the information need expressed in natural language. In this paper, we propose a query formulation model able of 1) translating natural language expressions to keyword queries in a supervised manner, and 2) injecting relevance feedback in the learning process through reinforcement learning technics. To overcome the lack of training data, we consider the translation model as a word selection process. Our model is evaluated on two TREC collections to demonstrate its effectiveness.
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Contributor : Laure Soulier <>
Submitted on : Friday, June 7, 2019 - 12:48:39 PM
Last modification on : Tuesday, March 23, 2021 - 9:28:03 AM


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  • HAL Id : hal-02150653, version 1


Wafa Aissa, Laure Soulier, Ludovic Denoyer. Modèle de compréhension du besoin en information pour la RI conversationnelle. CORIA 2019 - 16ème COnférence en Recherche d’Information et Applications, Mar 2019, Lyon, France. ⟨hal-02150653⟩



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