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Conference Papers Year : 2019

Modal sense classification with task-specific context embeddings


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.
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hal-02143762 , version 1 (29-05-2019)


  • HAL Id : hal-02143762 , version 1


Bo Li, Mathieu Dehouck, Pascal Denis. Modal sense classification with task-specific context embeddings. ESANN 2019 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2019, Bruges, Belgium. ⟨hal-02143762⟩
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