Expansion de requêtes à base de motifs et de Word Embeddings pour améliorer la recherche de microblogs

Abstract : Social microblogging services have an especially significant role in our society. Twitter is one of the most popular microblogging sites used by people to find relevant information (e.g., breaking news, popular trends, information about people of interest, etc). In this context, retrieving information from such data has recently gained growing attention and opening new challenges. However, the size of such data and queries is usually short and may impact the search result. Query Expansion (QE) has the main task in this issue. In fact, words can have different meanings where only one is used for a given context. In this paper, we propose a QE method by considering the meaning of the context. Thus, we use patterns and Word Embeddings to expand users' queries. We experiment and evaluate the proposed method on the TREC dataset. Results show the effectiveness of the proposed approach and signify the combination of patterns and word embedding for enhanced microblog retrieval.
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Submitted on : Friday, April 5, 2019 - 11:37:03 AM
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Meryem Bendella, Mohamed Quafafou. Expansion de requêtes à base de motifs et de Word Embeddings pour améliorer la recherche de microblogs. COnférence en Recherche d'Informations et Applications - CORIA 2019, 16th French Information Retrieval Conference, Mar 2019, Lyon, France. ⟨hal-02090899⟩

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