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A Simulation-Based Slope Metric for Anchor List Reliability in Word Embedding Spaces

Author

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  • Taylor, Marshall A.

    (New Mexico State University)

  • Stoltz, Dustin S.

    (Lehigh University)

  • Harper, Heather
  • Kumar, Sanuj
  • Nandhikonda, Sumanth Reddy
  • Burks, Luke

Abstract

Inducing semantic relations in word vector spaces and analyzing how other words or entire documents discursively engage these relations is a popular form of cultural analysis. We propose a reliability metric that is easily interpretable and agnostic to the type of relation. The metric, which we call the anchor reliability coefficient (or relco), is found by creating a synthetic document-term matrix of simulated documents that sequentially shift more of their probability mass from relation-relevant anchor terms to randomly drawn words, and then regressing the documents' similarity to an induced relation by the inverse randomness rank of the documents. We validate the metric at the word-level with both expert- and crowd-sourced dictionaries and at the document-level with expert-annotated social media posts.

Suggested Citation

  • Taylor, Marshall A. & Stoltz, Dustin S. & Harper, Heather & Kumar, Sanuj & Nandhikonda, Sumanth Reddy & Burks, Luke, 2025. "A Simulation-Based Slope Metric for Anchor List Reliability in Word Embedding Spaces," SocArXiv sc2ub_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:sc2ub_v1
    DOI: 10.31219/osf.io/sc2ub_v1
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    References listed on IDEAS

    as
    1. Stijn Daenekindt & Julian Schaap, 2022. "Using word embedding models to capture changing media discourses: a study on the role of legitimacy, gender and genre in 24,000 music reviews, 1999–2021," Journal of Computational Social Science, Springer, vol. 5(2), pages 1615-1636, November.
    2. Lenth, Russell V., 2016. "Least-Squares Means: The R Package lsmeans," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i01).
    3. repec:osf:socarx:36r2d_v1 is not listed on IDEAS
    4. Rachel Kahn Best & Alina Arseniev-Koehler, 2023. "The Stigma of Diseases: Unequal Burden, Uneven Decline," American Sociological Review, , vol. 88(5), pages 938-969, October.
    5. Alina Arseniev-Koehler & Jacob G. Foster, 2022. "Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat," Sociological Methods & Research, , vol. 51(4), pages 1484-1539, November.
    6. 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.
    7. Marshall A. Taylor & Dustin S. Stoltz, 2025. "A workflow for analyzing cultural schemas in texts," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 49(1), pages 1-24, January.
    8. Taylor, Marshall A. & Stoltz, Dustin S., 2020. "Integrating Semantic Directions with Concept Mover's Distance to Measure Binary Concept Engagement," SocArXiv 36r2d, Center for Open Science.
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