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Word2vec has racked up plenty of citations because it satisifies both of Kuhn\u2019s 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 \u2018How does word2vec work\u2019 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).<\/jats:p>","DOI":"10.1017\/s1351324916000334","type":"journal-article","created":{"date-parts":[[2016,12,16]],"date-time":"2016-12-16T09:37:26Z","timestamp":1481881046000},"page":"155-162","source":"Crossref","is-referenced-by-count":920,"title":["Word2Vec"],"prefix":"10.1017","volume":"23","author":[{"given":"KENNETH WARD","family":"CHURCH","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2016,12,16]]},"reference":[{"key":"S1351324916000334_ref022","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1613\/jair.3640","article-title":"Domain and function: a dual-space model of semantic relations and compositions","volume":"44","author":"Turney","year":"2012","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S1351324916000334_ref001","unstructured":"Bolukbasi T. , Chang K.-W. , Zou J. , Venkatesh Saligrama Adam and Kalai 2016. 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