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                <term xml:lang="en">Information Extraction</term>
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                <term xml:lang="fr">Liaison d'entité</term>
                <term xml:lang="fr">Embeddings de mots et d'entités</term>
                <term xml:lang="fr">Extraction d'information</term>
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              <p>Combining Word and Entity Embeddings for Entity Linking. 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. However, systems have to deal with ambiguity as numerous entities could be linked to a mention. This paper proposes a novel method for entity disambiguation 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. </p>
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              <p>La désambiguïsation d'entités (ou liaison d'entités), qui consiste à relier des mentions d'entités d'un texte à des entités d'une base de connaissance, est un problème qui se pose, entre autre, pour le peuplement automatique de bases de connaissances  à partir de textes. Une difficulté  de cette tâche est la résolution d'ambiguïtés car les systèmes ont à choisir parmi un nombre important de candidats. Cet article propose une nouvelle approche fondée sur l'apprentissage joint de représentations distribuées des mots et des entités dans le même espace, ce qui permet d'établir un modèle robuste pour la comparaison entre le contexte local de la mention d'entité et les entités candidates.</p>
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