Using pseudo-senses for improving the extraction of synonyms from word embeddings

Olivier Ferret


Abstract
The methods proposed recently for specializing word embeddings according to a particular perspective generally rely on external knowledge. In this article, we propose Pseudofit, a new method for specializing word embeddings according to semantic similarity without any external knowledge. Pseudofit exploits the notion of pseudo-sense for building several representations for each word and uses these representations for making the initial embeddings more generic. We illustrate the interest of Pseudofit for acquiring synonyms and study several variants of Pseudofit according to this perspective.
Anthology ID:
P18-2056
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
351–357
Language:
URL:
https://aclanthology.org/P18-2056
DOI:
10.18653/v1/P18-2056
Bibkey:
Cite (ACL):
Olivier Ferret. 2018. Using pseudo-senses for improving the extraction of synonyms from word embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 351–357, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Using pseudo-senses for improving the extraction of synonyms from word embeddings (Ferret, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2056.pdf
Presentation:
 P18-2056.Presentation.pdf
Video:
 https://aclanthology.org/P18-2056.mp4