
Andrew Cowell
· ProfessorVerifiedUniversity of Colorado Boulder · Linguistics
Active 1851–2025
About
Andrew Cowell (PhD, UC Berkeley 1993) is a Professor of Linguistics and Faculty Director of the Center for Native American and Indigenous Studies (CNAIS) at the University of Colorado Boulder. His work focuses on linguistic anthropology and language documentation, with primary research on the Arapaho language. He has also worked on Gros Ventre and Miwok languages, and has an interest in Polynesian languages, particularly those of Hawaii and Tahiti. Cowell has published numerous articles and books, and has developed curricular materials and websites aimed at language and culture learning and documentation. His current project involves developing a lexical database of Arapaho, supported by funding from the NSF/NEH DEL program.
Research topics
- Computer Science
- Natural Language Processing
- Artificial Intelligence
- Linguistics
- Information Retrieval
- World Wide Web
- Epistemology
- Social psychology
- Philosophy
- Psychology
- Geography
- Mathematics
- Combinatorics
Selected publications
A database could help revive the Arapaho language before its last speakers are gone
2025-11-26
article1st authorCorrespondingDictionaries · 2025-01-01
articleSenior authorABSTRACT: UMR (Uniform Meaning Representation) is a powerful new tool for representing the lexis-semantics interface graphically in a cross-linguistically uniform way. Its primary purpose is to support semantic parsing in natural language processing (NLP), but it is also a useful tool for documenting and analyzing semantic structures in language. In this paper, we demonstrate strengths and weaknesses of UMR in annotation of Arapaho, a highly polysynthetic and agglutinating Algonquian language. We describe lexicographical design tensions that emerge when applying UMR to languages like Arapaho and offer suggestions for refining UMR so that its goal of applying cross-linguistically can be fully realized. These tensions center on the need for a special Arapaho-UMR lexicon to guide annotators in carving up morphologically complex words into distinct predicate and argument structures in UMR graphs. Such a resource is critical for polysynthetic and agglutinating languages in which a single word may encode an entire complex proposition. We propose such a lexicon and demonstrate how it will help annotators stay as they navigate morphological complexity inside and outside the verb stem.
The Aaniiih (Gros Ventre) Language
2024-10-01
book1st authorCorrespondingBootstrapping UMR Annotations for Arapaho from Language Documentation Resources
2024-05-01
articleThe Phonology of the NoowoθineheenoɁ
Michigan State University Press eBooks · 2023-03-01
book-chapter1st authorCorrespondingHow Many Classifiers in Arapaho?
Michigan State University Press eBooks · 2022-04-01
book-chapter1st authorCorrespondingIndigenous Languages of the West:
University of Arizona Press eBooks · 2022-05-17
book-chapter1st authorCorrespondingProceedings of the International AAAI Conference on Web and Social Media · 2021 · 3 citations
Senior authorCorresponding- Computer Science
- Information Retrieval
- Computer Science
Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member’s interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.
IRL - University of Missouri, St. Louis (University of Missouri–St. Louis) · 2021-01-01
articleOpen accessSenior authorThis co-authored dissertation is a macro-level case study of a public high school tracking system and a micro-level autoethnography from a music educator about vocal music placement practices. The case study sought to comprehensively describe and analyze the characteristics of a tracking system in all core subjects at a single school, including the extent of differentiation of levels, placement practices, student mobility, teacher tracking, and inclusiveness by race, class, and gender. It also used network analysis software to map more than 75,000 connections among students created by their course-taking; it used this to quantitatively identify student communities, which then were analyzed for demographic trends. Paired with the case study, the autoethnography examined the assumptions and placement practices in high school vocal music and in educator preparation programs. The case study found limited student mobility, complex placement practices that differed from one subject area to another, extensive segregation in nearly all subject areas, and limited evidence for teacher tracking. It also revealed several student communities that function as segregated schools-within-a-school. The autoethnography revealed the impact of teachers as evaluator on the leveling and ability grouping practices within vocal music education and specifically highlights bias through the lens of Critical Race Theory. Recommendations for policy reform are provided.
Designing a Uniform Meaning Representation for Natural Language Processing
KI - Künstliche Intelligenz · 2021 · 38 citations
- Computer Science
- Natural Language Processing
- Computer Science
Frequent coauthors
- 16 shared
Noah D. Guynn
- 16 shared
Guillaume De
Hernia Center
- 16 shared
Matthew Fisher
Hernia Center
- 16 shared
Simon Gaunt
- 16 shared
Rosalind Brown
ECW Press (Canada)
- 16 shared
Deborah McGrady
- 9 shared
Michelle Gregory
Biological Control of Insects Research Laboratory
- 8 shared
David A. Thurman
Education
- 1993
Ph.D.
UC Berkeley
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Andrew Cowell
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup