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Su Lin Blodgett
Researcher at University of Massachusetts Amherst
Publications - 29
Citations - 1322
Su Lin Blodgett is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Computer science & Language identification. The author has an hindex of 10, co-authored 19 publications receiving 697 citations. Previous affiliations of Su Lin Blodgett include Microsoft.
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Language (Technology) is Power: A Critical Survey of "Bias" in NLP
TL;DR: The authors survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing bias is an inherently normative process.
Proceedings ArticleDOI
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
TL;DR: A greater recognition of the relationships between language and social hierarchies is urged, encouraging researchers and practitioners to articulate their conceptualizations of “bias” and to center work around the lived experiences of members of communities affected by NLP systems.
Proceedings ArticleDOI
Demographic Dialectal Variation in Social Media: A Case Study of African-American English
TL;DR: A case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter and proposes a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and verifies that this language follows well-known AAE linguistic phenomena.
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Demographic Dialectal Variation in Social Media: A Case Study of African-American English
TL;DR: This paper proposed a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and verified that this language follows well-known AAE linguistic phenomena.
Posted Content
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English.
Su Lin Blodgett,Brendan O'Connor +1 more
TL;DR: An empirical analysis of racial disparity in language identification for tweets written in African-American English is conducted, and implications of disparity in NLP are discussed.