Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources
Jiang Hong Guo, Wanxiang Che, Haifeng Wang, Ting Liu · International Conference on Computational Linguistics · 2014
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.