Embedding Words and Senses Together via Joint Knowledge-Enhanced Training

Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, Roberto Navigli

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.
Anthology ID:
K17-1012
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Roger Levy, Lucia Specia
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–111
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/K17-1012/
DOI:
10.18653/v1/K17-1012
Bibkey:
Cite (ACL):
Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, and Roberto Navigli. 2017. Embedding Words and Senses Together via Joint Knowledge-Enhanced Training. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 100–111, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training (Mancini et al., CoNLL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/K17-1012.pdf
Presentation:
 K17-1012.Presentation.pdf
Data
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison
PDF Search Presentation Fix metadata