A bottom up approach to category mapping and meaning change

Haim Dubossarsky, Yulia Tsvetkov, Chris Dyer, Eitan Grossman

Research output: Contribution to conferencePaperpeer-review

29 Scopus citations

Abstract

In this article, we use an automated bottom- up approach to identify semantic categories in an entire corpus. We conduct an experiment using a word vector model to represent the meaning of words. The word vectors are then clustered, giving a bottom-up representation of semantic categories. Our main finding is that the likelihood of changes in a word's meaning correlates with its position within its cluster.

Original languageEnglish
Pages66-70
Number of pages5
StatePublished - 2015
EventConference on Word Knowledge and Word Usage: Representations and Processes in the Mental Lexicon, NetWordS 2015 - Pisa, Italy
Duration: 30 Mar 20151 Apr 2015

Conference

ConferenceConference on Word Knowledge and Word Usage: Representations and Processes in the Mental Lexicon, NetWordS 2015
Country/TerritoryItaly
CityPisa
Period30/03/151/04/15

Bibliographical note

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Copyright © by the paper's authors.

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