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Integrating semantic directions with concept mover’s distance to measure binary concept engagement

Author

Listed:
  • Marshall A. Taylor

    (New Mexico State University)

  • Dustin S. Stoltz

    (Lehigh University)

Abstract

In an earlier article published in this journal (“Concept Mover’s Distance”, 2019), we proposed a method for measuring concept engagement in texts that uses word embeddings to find the minimum cost necessary for words in an observed document to “travel” to words in a “pseudo-document” consisting only of words denoting a concept of interest. One potential limitation we noted is that, because words associated with opposing concepts will be located close to one another in the embedding space, documents will likely have similar closeness to starkly opposing concepts (e.g., “life” and “death”). Using aggregate vector differences between antonym pairs to extract a direction in the semantic space pointing toward a pole of the binary opposition (following “The Geometry of Culture,” American Sociological Review, 2019), we illustrate how CMD can be used to measure a document’s engagement with binary concepts.

Suggested Citation

  • Marshall A. Taylor & Dustin S. Stoltz, 2021. "Integrating semantic directions with concept mover’s distance to measure binary concept engagement," Journal of Computational Social Science, Springer, vol. 4(1), pages 231-242, May.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00075-8
    DOI: 10.1007/s42001-020-00075-8
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    References listed on IDEAS

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    1. Nikhil Garg & Londa Schiebinger & Dan Jurafsky & James Zou, 2018. "Word embeddings quantify 100 years of gender and ethnic stereotypes," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(16), pages 3635-3644, April.
    2. Dustin S. Stoltz & Marshall A. Taylor, 2019. "Concept Mover’s Distance: measuring concept engagement via word embeddings in texts," Journal of Computational Social Science, Springer, vol. 2(2), pages 293-313, July.
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    Cited by:

    1. Oscar Stuhler, 2022. "Who Does What to Whom? Making Text Parsers Work for Sociological Inquiry," Sociological Methods & Research, , vol. 51(4), pages 1580-1633, November.

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