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Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications
Most unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences.
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings
Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable.
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