Learning sense-specificword embeddings by exploiting bilingual resources

Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails to capture polysemy. In this paper, we address this problem by representing words with multiple and sense-specific embeddings, which are learned from bilingual parallel data. We evaluate our embeddings using the word similarity measurement and show that our approach is significantly better in capturing the sense-level word similarities. We further feed our embeddings as features in Chinese named entity recognition and obtain noticeable improvements against single embeddings.