J
Jeevan Shankar
Researcher at University of Massachusetts Amherst
Publications - 2
Citations - 494
Jeevan Shankar is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Word (computer architecture) & Context (language use). The author has an hindex of 2, co-authored 2 publications receiving 462 citations.
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Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
TL;DR: An extension to the Skip-gram model that efficiently learns multiple embeddings per word type is presented, and its scalability is demonstrated by training with one machine on a corpus of nearly 1 billion tokens in less than 6 hours.
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Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
TL;DR: This paper proposed an extension to the Skip-gram model that efficiently learns multiple embeddings per word type, which differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the number of senses per word and by its efficiency and scalability.