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

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. MOTS-CLÉS : Recherche d’entités, Désambiguïsation d’entités, Sélection d’exemples négatifs.