Modal sense classification with task-specific ...
Type de document :
Communication dans un congrès avec actes
Titre :
Modal sense classification with task-specific context embeddings
Auteur(s) :
Li, Bo [Auteur]
Machine Learning in Information Networks [MAGNET]
Dehouck, Mathieu [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Machine Learning in Information Networks [MAGNET]
Denis, Pascal [Auteur]
Machine Learning in Information Networks [MAGNET]
Machine Learning in Information Networks [MAGNET]
Dehouck, Mathieu [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Machine Learning in Information Networks [MAGNET]
Denis, Pascal [Auteur]
Machine Learning in Information Networks [MAGNET]
Titre de la manifestation scientifique :
ESANN 2019 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Ville :
Bruges
Pays :
Belgique
Date de début de la manifestation scientifique :
2019-04-24
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Apprentissage [cs.LG]
Résumé en anglais : [en]
Sense disambiguation of modal constructions is a crucial part of natural language understanding. Framed as a supervised learning task, this problem heavily depends on an adequate feature representation of the modal verb ...
Lire la suite >Sense disambiguation of modal constructions is a crucial part of natural language understanding. Framed as a supervised learning task, this problem heavily depends on an adequate feature representation of the modal verb context. Inspired by recent work on general word sense disambiguation, we propose a simple approach of modal sense classification in which standard shallow features are enhanced with task-specific context embedding features. Comprehensive experiments show that these enriched contextual representations fed into a simple SVM model lead to significant classification gains over shallow feature sets.Lire moins >
Lire la suite >Sense disambiguation of modal constructions is a crucial part of natural language understanding. Framed as a supervised learning task, this problem heavily depends on an adequate feature representation of the modal verb context. Inspired by recent work on general word sense disambiguation, we propose a simple approach of modal sense classification in which standard shallow features are enhanced with task-specific context embedding features. Comprehensive experiments show that these enriched contextual representations fed into a simple SVM model lead to significant classification gains over shallow feature sets.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- ESANN-paper.pdf
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