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Modèles neuronaux pour la recherche d'information : approches dirigées par les ressources sémantiques

Gia Hung Nguyen 1
1 IRIT-IRIS - Recherche d’Information et Synthèse d’Information
IRIT - Institut de recherche en informatique de Toulouse
Abstract : In this thesis, we focus on bridging the semantic gap between the documents and queries representations, hence improve the matching performance. We propose to combine relational semantics from knowledge resources and distributed semantics of the corpus inferred by neural models. Our contributions consist of two main aspects: (1) Improving distributed representations of text for IR tasks. We propose two models that integrate relational semantics into the distributed representations: a) an offline model that combines two types of pre-trained representations to obtain a hybrid representation of the document; b) an online model that jointly learns distributed representations of documents, concepts and words. To better integrate relational semantics from knowledge resources, we propose two approaches to inject these relational constraints, one based on the regularization of the objective function, the other based on instances in the training text. (2) Exploiting neural networks for semantic matching of documents}. We propose a neural model for document-query matching. Our neural model relies on: a) a representation of raw-data that models the relational semantics of text by jointly considering objects and relations expressed in a knowledge resource, and b) an end-to-end neural architecture that learns the query-document relevance by leveraging the distributional and relational semantics of documents and queries.
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Gia Hung Nguyen. Modèles neuronaux pour la recherche d'information : approches dirigées par les ressources sémantiques. Informatique et langage [cs.CL]. Université Paul Sabatier - Toulouse III, 2018. Français. ⟨NNT : 2018TOU30233⟩. ⟨tel-02507902⟩

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