A Mixture Model for Learning Multi-Sense Word Embeddings
This work addresses the challenge of improving word representation quality in natural language processing, though it appears incremental as it builds on existing multi-sense embedding methods.
The paper tackles the problem of learning word embeddings that account for multiple senses of words, proposing a mixture model that outperforms previous models on standard evaluation tasks.
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.