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

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Abstract

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.
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Dates and versions

hal-01841594 , version 1 (17-07-2018)
hal-01841594 , version 2 (07-06-2019)

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Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie 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⟩
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