Search Results for author: Amol Agrawal

Found 2 papers, 0 papers with code

Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications

no code implementations29 Mar 2021 Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum

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.

Extractive Summarization Sentence Similarity +1

Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings

no code implementations WS 2018 Haw-Shiuan Chang, Amol Agrawal, Ananya Ganesh, Anirudha Desai, Vinayak Mathur, Alfred Hough, Andrew McCallum

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.

Word Sense Induction

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