Désambiguïsation d’entités nommées par apprentissage de modèles d’entités à large échelle - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

Désambiguïsation d’entités nommées par apprentissage de modèles d’entités à large échelle

(1, 2) , (1) , (1) , (1) , (1) , (1, 3)
1
2
3

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
Fichier principal
Vignette du fichier
23.pdf (179.03 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

cea-01857881 , version 1 (17-08-2018)

Identifiers

  • HAL Id : cea-01857881 , version 1

Cite

Hani Daher, Romaric Besançon, Olivier Ferret, Hervé Le Borgne, Anne-Laure Daquo, et al.. Désambiguïsation d’entités nommées par apprentissage de modèles d’entités à large échelle. 14ème COnférence en Recherche d'Information et Applications (CORIA 2017), 2017, Marseille, France. pp.185-200. ⟨cea-01857881⟩
142 View
192 Download

Share

Gmail Facebook Twitter LinkedIn More