Dan Jurafsky
Affiliations: | Computer Science | Stanford University, Palo Alto, CA |
Area:
natural language understandingWebsite:
https://explorecourses.stanford.edu/instructor/jurafskyGoogle:
"Dan Jurafsky"Cross-listing: Computer Science Tree
Children
Sign in to add traineeMichelle L. Gregory | grad student | 2001 | CU Boulder |
Douglas W. Roland | grad student | 2001 | CU Boulder |
Patrick J. Schone | grad student | 2001 | CU Boulder |
Noah B. Coccaro | grad student | 2005 | CU Boulder |
T. Florian Jaeger | grad student | 2006 | Stanford |
Tim Florian Jaeger | grad student | 2005-2006 | Stanford |
Daniel Cer | grad student | 2011 | CU Boulder |
Uriel Cohen Priva | grad student | 2006-2012 | Stanford |
Ruihong Huang | post-doc | ||
Sebastian Padó | post-doc | ||
Steven Bethard | post-doc | 2009-2010 | Stanford (Computer Science Tree) |
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Publications
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Card D, Chang S, Becker C, et al. (2022) Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration. Proceedings of the National Academy of Sciences of the United States of America. 119: e2120510119 |
Mendelsohn J, Tsvetkov Y, Jurafsky D. (2020) A Framework for the Computational Linguistic Analysis of Dehumanization. Frontiers in Artificial Intelligence. 3: 55 |
Miner AS, Haque A, Fries JA, et al. (2020) Assessing the accuracy of automatic speech recognition for psychotherapy. Npj Digital Medicine. 3: 82 |
Turnwald BP, Anderson KG, Jurafsky D, et al. (2020) Five-star prices, appealing healthy item descriptions? Expensive restaurants' descriptive menu language. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association |
Miner AS, Haque A, Fries JA, et al. (2020) Assessing the accuracy of automatic speech recognition for psychotherapy. Npj Digital Medicine. 3: 82 |
Koenecke A, Nam A, Lake E, et al. (2020) Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences of the United States of America |
Hahn M, Jurafsky D, Futrell R. (2020) Universals of word order reflect optimization of grammars for efficient communication. Proceedings of the National Academy of Sciences of the United States of America |
Pryzant R, Diehl Martinez R, Dass N, et al. (2020) Automatically Neutralizing Subjective Bias in Text Proceedings of the Aaai Conference On Artificial Intelligence. 34: 480-489 |
Lucy L, Demszky D, Bromley P, et al. (2020) Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks: Aera Open. 6: 233285842094031 |
Garg N, Schiebinger L, Jurafsky D, et al. (2018) Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences of the United States of America |