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arXiv · 1706.05111

A Mixture Model for Learning Multi-Sense Word Embeddings

Dai Quoc Nguyen·Dat Quoc Nguyen·Ashutosh Modi·Stefan Thater·Manfred Pinkal
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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.

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Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal. 2017-06-15. A Mixture Model for Learning Multi-Sense Word Embeddings. https://doi.org/10.18653/v1%2Fs17-1015

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