A Mixture Model for Learning Multi-Sense Word Embeddings
Computation and Language
2017-08-14 v1
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.
Cite
@article{arxiv.1706.05111,
title = {A Mixture Model for Learning Multi-Sense Word Embeddings},
author = {Dai Quoc Nguyen and Dat Quoc Nguyen and Ashutosh Modi and Stefan Thater and Manfred Pinkal},
journal= {arXiv preprint arXiv:1706.05111},
year = {2017}
}
Comments
*SEM 2017