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

Hani Daher 1, 2 Romaric Besançon 1 Olivier Ferret 1 Hervé Le Borgne 1 Anne-Laure Daquo 1 Youssef Tamaazousti 1, 3
1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs
2 LVIC - Laboratoire Vision et Ingénierie des Contenus
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
3 MICS - Mathématiques et Informatique pour la Complexité et les Systèmes
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
Keywords : Entity IR Entity retrieval Entity linking Negative sample selection
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Conference papers
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Submitted on : Friday, August 17, 2018 - 3:30:58 PM
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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⟩

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