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Queering NLP: A Non-Heteronormative Approach to Quantifying and Investigating Sentiment Bias against LGBTQ+ Identities in Word Embeddings

Head shot of Bang Nguyen

Name: Bang Nguyen
Major: Computer Science
Minor: Statistical & Data Sciences and Communication Studies
Advisor: Professor Kowshik Bhowmik; Professor Thomas Montelione (second reader)

Critical Digital Engagement Award

To view Bang’s Independent Study, please click the button below (Wooster log-in credentials required)

Bang Nguyen’s Independent Study

Posted in Comments Enabled, Independent Study, Symposium 2022.


2 responses to “Queering NLP: A Non-Heteronormative Approach to Quantifying and Investigating Sentiment Bias against LGBTQ+ Identities in Word Embeddings”

  1. Denise Bostdorff says:

    Congratulations!

  2. Jillian Morrison says:

    Bang, I enjoyed talking to you about your study (and will come talk some more this afternoon). I look forward to hopefully hearing (in the future) how context affects or doesn’t affect your results. I agree that training models based on biased data will only breathe a more biased world. I look forward to seeing you help solve some of these problems in the future!

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