A Mixture Model for Learning Multi-Sense Word Embeddings
Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal · 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.