A Mixture Model for Learning Multi-Sense Word Embeddings

Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal


Abstract
Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.
Anthology ID:
S17-1015
Volume:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
*SEM | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–127
Language:
URL:
https://aclanthology.org/S17-1015
DOI:
10.18653/v1/S17-1015
Bibkey:
Cite (ACL):
Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, and Manfred Pinkal. 2017. A Mixture Model for Learning Multi-Sense Word Embeddings. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 121–127, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
A Mixture Model for Learning Multi-Sense Word Embeddings (Nguyen et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-1015.pdf