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

Nguyen, Gia Hung and Tamine-Lechani, Lynda and Soulier, Laure and Souf, Nathalie A Tri-Partite Neural Document Language Model for Semantic Information Retrieval. (2018) In: 15th European Semantic Web Conference (ESWC 2018), 3 June 2018 - 7 June 2018 (Crète, Greece).

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Official URL: https://doi.org/10.1007/978-3-319-93417-4_29


Previous work in information retrieval have shown that using evidence, such as concepts and relations, from external knowledge sources could enhance the retrieval performance. Recently, deep neural approaches have emerged as state-of-the art models for capturing word semantics. 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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in Volume 10843 of Lecture Notes in Computer Science ISSN : 0302-9743 ISBN: 978-3-319-93416-7 The original PDF is available at: https://link.springer.com/chapter/10.1007/978-3-319-93417-4_29
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Sorbonne Université (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:30 Sep 2019 12:03

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