Combining word and entity embeddings for entity linking

Abstract : 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. The general approach for this task is to generate, for a given mention, a set of candidate entities from the base and, in a second step, determine which is the best one. This paper proposes a novel method for the second step 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. The relative improvement of this approach is experimentally validated on a recent benchmark corpus from the TAC-EDL 2015 evaluation campaign.
Type de document :
Communication dans un congrès
Springer. Extended Semantic Web Conference, Jan 2017, Portoroz, Slovenia. 〈10.1007/978-3-319-58068-5_21〉
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01626196
Contributeur : Limsi Publications <>
Soumis le : lundi 13 novembre 2017 - 13:04:25
Dernière modification le : lundi 24 septembre 2018 - 11:34:03
Document(s) archivé(s) le : mercredi 14 février 2018 - 13:57:56

Fichier

eswc17.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

José Moreno, Romaric Besancon, Romain Beaumont, Eva D'Hondt, Anne-Laure Ligozat, et al.. Combining word and entity embeddings for entity linking. Springer. Extended Semantic Web Conference, Jan 2017, Portoroz, Slovenia. 〈10.1007/978-3-319-58068-5_21〉. 〈hal-01626196〉

Partager

Métriques

Consultations de la notice

187

Téléchargements de fichiers

649