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Apprendre des représentations jointes de mots et d'entités pour la désambiguïsation d'entités

Abstract : Combining Word and Entity Embeddings for Entity Linking. The correct identification of the link between an entity mention in a text and a known entity in a large knowledge base is important in information retrieval or information extraction. However, systems have to deal with ambiguity as numerous entities could be linked to a mention. This paper proposes a novel method for entity disambiguation which is based on the joint learning of embeddings for the words in the text and the entities in the knowledge base. By learning these embeddings in the same space we arrive at a more conceptually grounded model that can be used for candidate selection based on the surrounding context.
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Submitted on : Monday, November 13, 2017 - 1:03:09 PM
Last modification on : Monday, July 4, 2022 - 10:15:51 AM
Long-term archiving on: : Wednesday, February 14, 2018 - 1:45:38 PM


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  • HAL Id : hal-01626197, version 1
  • OATAO : 22152


José G. Moreno, Romaric Besancon, Romain Beaumont, Eva d'Hondt, Anne-Laure Ligozat, et al.. Apprendre des représentations jointes de mots et d'entités pour la désambiguïsation d'entités. 24ème Conférence sur le Traitement Automatique des Langues Naturelles - TALN 2017, LLL (Laboratoire Ligérien de Linguistique); LIFO (Laboratoire d’Informatique Fondamentale d’Orléans); LI (Laboratoire Informatique) de Tours, Jan 2017, Orléans, France. ⟨hal-01626197⟩



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