Recently, word representation has been increasingly focused on for its
excellent properties in representing the word semantics. Previous works mainly
suffer from the problem of polysemy phenomenon...
To address this problem, most
of previous models represent words as multiple distributed vectors. However, it
cannot reflect the rich relations between words by representing words as points
in the embedded space. In this paper, we propose the Gaussian mixture skip-gram
(GMSG) model to learn the Gaussian mixture embeddings for words based on
skip-gram framework. Each word can be regarded as a gaussian mixture
distribution in the embedded space, and each gaussian component represents a
word sense. Since the number of senses varies from word to word, we further
propose the Dynamic GMSG (D-GMSG) model by adaptively increasing the sense
number of words during training. Experiments on four benchmarks show the
effectiveness of our proposed model.