A Tri-Partite Neural Document Language Model for Semantic Information Retrieval

Gia-Hung Nguyen (1) , Lynda Tamine (1) , Laure Soulier (2) , Nathalie Bricon-Souf (3)

1 IRIT-IRIS - Recherche d’Information et Synthèse d’Information
2 MLIA - Machine Learning and Information Access
3 IRIT-SIG - Systèmes d’Informations Généralisées

Previous work in information retrieval have shown that using evidence, such as concepts and relations, from external knowledge resources could enhance the retrieval performance. Recently, deep neu-ral approaches have emerged as state-of-the art models for capturing word semantics that can also be efficiently injected in IR models. This paper presents a new tri-partite neural document language framework that leverages explicit knowledge to jointly constrain word, concept, and document learning representations to tackle a number of issues including polysemy and granularity mismatch. We show the effectiveness of the framework in various IR tasks including document similarity, document re-ranking, and query expansion.

Mots clés: knowledge resource; deep learning; deep learn- ing; Semantic information retrieval; knowledge source

Domaines: Recherche d'information [cs.IR]

Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie Bricon-Souf. A Tri-Partite Neural Document Language Model for Semantic Information Retrieval. ESWC 2018 - 15th European Semantic Web Conference, Jun 2018, Heraklion, Crète, Greece. pp.445-461, ⟨10.1007/978-3-319-93417-4_29⟩. ⟨hal-01841594v2⟩