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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 on April 26, 2022.


3 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!

  3. Homepage says:

    … [Trackback]

    […] There you will find 53133 more Infos: wooster.edu/2022/04/26/bang-nguyen/ […]

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Related Areas of Study

Statistical & Data Sciences

Use statistics, math, and computer science to gain insights into data and solve real-world problems.

Major Minor

Computer Science

Solve complex problems with creative solutions using computer programming and applications

Major Minor

Communication Studies

Be an effective listener, writer, and speaker who can think critically and connect with audiences

Major Minor

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