Paper: Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources

ACL ID C14-1048
Title Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 represent- ing 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 ap- proach 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.