Learning Word Sense Embeddings from Word Sense Definitions
Table 5: Performances on Semeval-2007 coarse-grained all-words WSD task.
['System', 'F1']
After we train our model and word sense embeddings using sense embeddings to represent words in definitions, our approach outperforms Chen et al. It illustrates that our sense embeddings can actually distinguish different senses of a word and our approach can actually learn the semantics of senses from definitions.