Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim at generating only one embedding vector for each individual word, which, however, limits their effectiveness because huge amounts of words are polysemous (such as bank and star). To address this problem, it is necessary to build multi embedding vectors to represent different meanings of a word respectively. Some recent studies attempted to train multi-prototype word embeddings through clustering context window features of the word. However, due to a large number of parameters to train, these methods yield limited scalability and are inefficient to be trained with big data. In this paper, we introduce a much more efficient method for learning multi embedding vectors for polysemous words. In particular, we first propose to model word polysemy from a probabilistic perspective and integrate it with the highly efficient continuous Skip-Gram model. Under this framework, we design an Expectation-Maximization algorithm to learn the word's multi embedding vectors. With much less parameters to train, our model can achieve comparable or even better results on word-similarity tasks compared with conventional methods.
REF proposed a multi-prototype Skip-gram model and designed an Expectation-Maximization (EM) algorithm to do word sense disambiguation and learn sense embedding vectors iteratively.
A Probabilistic Model for Learning Multi-Prototype Word Embeddings
COLING
Computer Science