# A Mixture Model for Learning Multi-Sense Word Embeddings

## TL;DR

This paper introduces a mixture model for learning multi-sense word embeddings that accounts for different senses and their varying importance, leading to improved performance on standard evaluation tasks.

## Contribution

It presents a generalized mixture model that induces multiple senses of words with different weights, advancing previous multi-sense embedding methods.

## Key findings

- Our model outperforms previous models on standard evaluation tasks.
- It effectively captures multiple senses with varying importance.
- The approach improves the quality of word embeddings in semantic tasks.

## 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.