565 research outputs found
A Mixture Model for Learning Multi-Sense Word Embeddings
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.Comment: *SEM 201
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