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Continuous representation of words is a standard component in deep learning-based NLP models.
Contextual correlates of synonymy
Herbert Rubenstein and John B Goodenough · 1965
Earlier work this paper cites.
Category norms of verbal items in 56 categories a replication and extension of the connecticut category norms
William F Battig and William E Montague · 1969
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Bridging the gap between semantic theory and computational simulations: Proceedings of the esslli workshop on distributional lexical semantics
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Elia Bruni, Nam-Khanh Tran, and Marco Baroni · 2014
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