Caleb Belth
[keɪləb bɛlθ] (he/him)
Welcome! I am an Assistant Professor of Linguistics at the University of Utah. I recently defended my PhD dissertation at the University of Michigan. My undergraduate is from Purdue University.
My research is developing an algorithmic approach to phonology, in which phonological generalizations and representations are the result of learning algorithms grounded in independent psychological mechanisms. Informed by linguistic theory, psycholinguistics, and language acquisition, I use computational models as explicit, testable hypotheses. I evaluate my models on natural-language data, such as child-directed speech. In doing so, I compare the model’s behavior to linguistic analyses of the phenomenon and language acquisition results. Moreover, by taking an explicit, computational approach, my models make predictions, which I evaluate by comparing to human behavior in psycholinguistic experiments.
For my research, I have been awarded an NSF GRF, an NDSEG fellowship, and a Richard F. and Eleanor A. Towner Prize for Distinguished Academic Achievement.
Feel free to contact me at cbelth@umich.edu.
Publications
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A Learning-Based Account of Local Phonological Processes
Caleb Belth
Phonology, In Press.
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Towards a Learning-Based Account of Underlying Forms: A Case Study in Turkish
Caleb Belth
Society for Computation in Linguistics, 2023
[pdf] [code] -
The Greedy and Recursive Search for Morphological Productivity
Caleb Belth, Sarah Payne, Deniz Beser, Jordan Kodner, Charles Yang
CogSci, 2021
[pdf] [code] -
A Hidden Challenge of Link Prediction: Which Pairs to Check?
Caleb Belth, Alican Büyükçakır, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2020 (acceptance rate: 9.8%)
Selected as one of the best papers at ICDM 2020. Invited for publication at the KAIS Journal, Springer.
[conference pdf] [journal extension pdf] [code]
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Mining Persistent Activity in Continually Evolving Networks.
Caleb Belth, Xinyi (Carol) Zheng, Danai Koutra
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), August 2020 (acceptance rate 17%)
[pdf] [code] [video]
Also accepted for presentation at the 16th SIGKDD International Workshop on Mining and Learning with Graphs. -
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization.
Caleb Belth, Xinyi (Carol) Zheng, Jilles Vreeken, Danai Koutra
ACM The Web Conference (WWW), April 2020 (oral presentation, acceptance rate 19%)
[pdf] [code] [video]
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Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Muller, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2019 (acceptance rate: 9%)
[pdf] -
When to Remember Where You Came from: Node Representation Learning in Higher-order Networks
Caleb Belth, Fahad Kamran, Donna Tjandra, Danai Koutra
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019 (acceptance rate: 15%)
[pdf] [slides]
Also accepted for presentation at the 15th SIGKDD International Workshop on Mining and Learning with Graphs.
Recent Presentations
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A Learning-Based Account of Phonological Tiers.
Caleb Belth
Penn Linguistics Conference, 2023.
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Learning Non-Local Phonological Alternations via Automatic Creation of Tiers.
Caleb Belth
LSA Conference, 2022.
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How a proclivity for adjacency can drive the learning of non-local alternations.
Caleb Belth
MidPhon, 2022.
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Learning Non-Local Phonological Alternations via Automatic Creation of Tiers.
Caleb Belth
Cognitive Modeling and Computational Linguistics workshop at ACL, 2022.
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Searching for Morphological Productivity.
Sarah Payne, Caleb Belth, Jordan Kodner, & Charles Yang.
The 96th Meeting of the Linguistics Society of America, 2022.
