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Embedding words and senses together via joint knowledge-enhanced training

Mancini, Massimiliano, Camacho Collados, Jose, Iacobacci, Ignacio and Navigli, Roberto 2017. Embedding words and senses together via joint knowledge-enhanced training. Presented at: 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, 3rd-4th August 2017. Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017). Stroudsburg, PA: The Association for Computational Linguistics, pp. 100-111. 10.18653/v1/K17-1012

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Abstract

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 state-of-the-art word- and sense-based models.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: The Association for Computational Linguistics
ISBN: 978-1-945626-54-8
Last Modified: 14 Aug 2019 13:46
URI: http://orca.cf.ac.uk/id/eprint/114046

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