Word2Vec

Author:

CHURCH KENNETH WARD

Abstract

AbstractMy last column ended with some comments about Kuhn and word2vec. Word2vec has racked up plenty of citations because it satisifies both of Kuhn’s conditions for emerging trends: (1) a few initial (promising, if not convincing) successes that motivate early adopters (students) to do more, as well as (2) leaving plenty of room for early adopters to contribute and benefit by doing so. The fact that Google has so much to say on ‘How does word2vec work’ makes it clear that the definitive answer to that question has yet to be written. It also helps citation counts to distribute code and data to make it that much easier for the next generation to take advantage of the opportunities (and cite your work in the process).

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software

Reference23 articles.

1. Domain and function: a dual-space model of semantic relations and compositions;Turney;Journal of Artificial Intelligence Research,2012

2. Bolukbasi T. , Chang K.-W. , Zou J. , Venkatesh Saligrama Adam and Kalai 2016. Man is to computer programmer as woman is to homemaker? Quantifying and Reducing Stereotypes in Word Embeddings. To appear in Advances in Neural Information Processing Systems (NIPS).

3. Linguistic regularities in continuous space word representations;Mikolov;HLT-NAACL,2013

4. Levy O. , and Goldberg Y. 2014c. Word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv:1402.3722

5. An information-theoretic definition of similarity;Lin;ICML,1998

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