Embedding Words and Senses Together via Joint Knowledge-Enhanced Training

Embedding Words and Senses Together via Joint Knowledge-Enhanced Training

We propose a new model that jointly learns word and sense embeddings and represents them in a unified vector space by exploiting large corpora and knowledge obtained from semantic networks. We evaluate the main features of our approach qualitatively and quantitatively in various tasks, highlighting the advantages of the proposed method with respect to state-of-the-art word- and sense-based models.

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