Paper: A Probabilistic Model for Learning Multi-Prototype Word Embeddings

ACL ID C14-1016
Title A Probabilistic Model for Learning Multi-Prototype Word Embeddings
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 mu...