Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Massimiliano Mancini, José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli · 2017
Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora.However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector.We address this issue by proposing a new model which learns word and sense embeddings jointly.Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings.We evaluate the main features of our approach both qualitatively and quantitatively in a variety of tasks, highlighting the advantages of the proposed method in comparison to stateof-the-art word-and sense-based models.