Breaking the Binary: Evaluating Gender Inclusivity in Neural Coreference Algorithms
Individual author or multiple authors
Individual
Major
Linguistics (LING)
Category of Work
Comps
Additional Category of Work
None
Degree
Bachelor of Arts
Class Year
2023
Comps Adviser(s)
Ussery, Cherlon
Identifier (Includes All Files and Enter All Their Files Name)
campl_2023_LING_comps.pdf
Keywords
Natural Language Processing, Machine Learning, Gender Bias, Computational Linguistics
Academic Civic Engagement (ACE) Comps Designation
yes
Format
application/pdf
Files Uploaded
Text (paper)
Rights Management
Student author/s retain copyright to this work. Through online submission process, author/s granted Carleton College the non-exclusive rights to preserve this work as part of Carleton's academic history and to use it for teaching purposes and/or institutional research and assessment.
