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
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–111
Language:
URL:
https://www.aclweb.org/anthology/K17-1012
DOI:
10.18653/v1/K17-1012
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/K17-1012.pdf
Presentation:
 K17-1012.Presentation.pdf