Computational linguistics

Computational linguistics at UMass Amherst involves the application of computational methods and theory to the study of linguistics, and the development of computational theories of language learning and language processing. The faculty in this area are part of a broader community of researchers studying computation and language at the 5 Colleges, which along with computational linguistics also includes research in natural language processing and information retrieval.


Michael Becker
BeckerResearch interests:
Phonology, computational and experimental morphophonology, fieldwork, Semitic
Rajesh Bhatt
BhattResearch interests:
Syntax, Semantics, Tree Adjoining Grammars, South Asian Languages
Brian Dillon
DillonResearch interests:
Psycholinguistics, Syntax
Gaja Jarosz
JaroszResearch interests:
Phonology, Learnability, Computational Modeling, Acquisition
Joe Pater
PaterResearch interests:
Phonological Theory and Learning, Computational and Experimental Methods
Kristine Yu
YuResearch interests:
Prosody from the Speech Signal on Up, Phonetics, Phonology

Graduate students

Cerys Hughes
SongResearch interests:
Computational Modeling, Phonology
Year started:
Seoyoung Kim
KimResearch interests:
Computational phonology, Fieldwork
Year started:
Andrew Lamont
LamontResearch interests:
Phonology, typology, computational linguistics
Year started:
Seoyoung Kim
KimResearch interests:
Computational phonology, Fieldwork
Year started:
Seung Suk (Josh) Lee
LeeResearch interests:
Phonetics, Phonology and Computational Linguistics
Year started:
Max Nelson
NelsonResearch interests:
Computational Linguistics, Phonology, Learnability
Year started:
Alex Nyman
NymanResearch interests:
Learnability and formal language theory
Year started:
Yixiao Song
SongResearch interests:
Computational Linguistics and Semantics
Year started:

Recent dissertations

Caroline Anderson. 2021. Shifting the Perspectival Landscape: Methods for Encoding, Identifying, and Selecting Perspectives.

Brandon Prickett. 2021. Learning phonology with sequence-to-sequence neural networks.

Coral Hughto. 2019. Emergent typological effects of agent-based learning models in Maximum Entropy Grammar.

Claire Moore-Cantwell. 2016. The representation of probabilistic phonological patterns: Neurological, behavioral, and computational evidence from the English stress system.

Aleksei Nazarov. 2016. Extending Hidden Structure: Features, Opacity, and Exceptions.

Presley Pizzo. 2015. Investigating Properties of Phonotactic Knowledge Through Web-Based Experimentation.

Robert Staubs, 2014. Computational Modeling of Learning Biases in Stress Typology.