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

A Mixture Model for Learning Multi-Sense Word Embeddings

Association for Computational Linguistics, pp. 121-127, Vancouver, Canada, 2017.

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.