On Modeling Sense Relatedness in Multi-prototype Word Embedding

IJCNLP 2017 Yixin CaoJiaxin ShiJuanzi LiZhiyuan LiuChengjiang Li

To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model. However, most related work ignores the relatedness among word senses which actually plays an important role... (read more)

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