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Conference papers
Désambiguïsation d’entités nommées par apprentissage de modèles d’entités à large échelle
Abstract : The objective of Entity Linking is to connect an entity mention in a text to a known
entity in a knowledge base. The general approach for this task is to generate, for a given
mention, a set of candidate entities from the base and determine, in a second step, the best
one. This paper focuses on this last step and proposes a method based on learning a function
that discriminates an entity from its most ambiguous ones. We adopt a model that is able to
deal with large knowledge bases. Thus our contribution lies in the strategy to learn efficiently
such a model. We propose three strategies with different efficiency/performance tradeoff. The
approach is experimentally validated on six datasets of the TAC evaluation campaigns by using
Freebase and DBpedia as reference knowledge bases
Cited literature [22 references]
https://hal-cea.archives-ouvertes.fr/cea-01857881
Contributor : Olivier Ferret <>
Submitted on : Friday, August 17, 2018 - 3:30:58 PM
Last modification on : Thursday, July 2, 2020 - 9:12:02 AM
Contributor : Olivier Ferret <>
Submitted on : Friday, August 17, 2018 - 3:30:58 PM
Last modification on : Thursday, July 2, 2020 - 9:12:02 AM
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