Queering NLP: A Non-Heteronormative Approach to Quantifying and Investigating Sentiment Bias against LGBTQ+ Identities in Word Embeddings
狈补尘别:听Bang Nguyen
惭补箩辞谤:听Computer Science
惭颈苍辞谤:听Statistical & Data Sciences and Communication Studies
础诲惫颈蝉辞谤:听Professor Kowshik Bhowmik; Professor Thomas Montelione (second reader)
To view Bang’s Independent Study, please click the button below (黑料老司机 log-in credentials required)
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”
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Statistical & Data Sciences
Use statistics, math, and computer science to gain insights into data and solve real-world problems.
Major MinorComputer Science
Solve complex problems with creative solutions using computer programming and applications
Major MinorCommunication Studies
Be an effective listener, writer, and speaker who can think critically and connect with audiences
Major Minor
Congratulations!
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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