Position Title
Professor and Chair
Education
- Ph.D., Language Technologies, Carnegie Mellon University, 2006
- M.S., Language Technologies, Carnegie Mellon University, 2003
About
Kenji Sagae is a computational linguist with research interests that include data-driven modeling of language phenomena, corpus methods, and application of language processing approaches to various forms of language analysis. His current work includes the application of neural models to analysis of syntax and semantics with linguistically expressive formalisms, and multilingual analysis of child language development. He received a PhD in language technologies from Carnegie Mellon University, and prior to joining UC Davis in 2016, he was a startup co-founder and a faculty member of the University of Southern California.
Research Focus
Computational linguistics and natural language processing
Selected Publications
(More publications available through Google Scholar)
Taiqi He, Megan A Boudewyn, John E Kiat, Kenji Sagae, Steven J Luck. (2022). Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials. Psychophysiology, 59(3). (preprint)
Kenji Sagae. (2021). Tracking Child Language Development with Neural Network Language Models. Frontiers in Psychology, 12.
Dian Yu, Taiqi He and Kenji Sagae. (2021). Language embeddings for typology and cross-lingual transfer learning. Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL 2021).
Dian Yu and Kenji Sagae. (2021). Automatically Exposing Problems with Neural Dialog Models. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2021).
Justin Garten, Brendan Kennedy, Kenji Sagae and Morteza Dehghani. (2019). Measuring the importance of context when modeling language comprehension. Behavior Research Methods.
Casey Casalnuovo, Kenji Sagae, Prem Devanbu. (2018). Studying the difference between natural and programming language corpora. Empirical Software Engineering.
Garten, J., Kennedy, B., Hoover, J., Sagae, K., Dehghani, M. (2018). Incorporating Demographic Embeddings into Language Understanding. Cognitive Science.
Ashish Vaswani* and Kenji Sagae*. Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States. Transactions of the Association for Computational Linguistics (TACL), 4, 183-196, 2016.
S. Lubetich and K. Sagae. Data-driven measurement of child language development. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 2151--2160, 2014.
L. Huang and K. Sagae. Dynamic programming for linear-time incremental parsing. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1077--1086. 2010.
K. Sagae, E. Davis, A. Lavie, B. Macwhinney, and S. Wintner. Morphosyntactic annotation of childes transcripts. Journal of child language, 37(03):705--729, 2010. Copyright Cambridge University Press. (direct link to online edition of the journal)
K. Sagae and J. Tsujii. Shift-reduce dependency DAG parsing. In Proceedings of the 22nd International Conference on Computational Linguistics (COLING) - Volume 1, pages 753--760. 2008.
K. Sagae and J. Tsujii. Dependency parsing and domain adaptation with LR models and parser ensembles. Proceedings of the CoNLL shared task session of EMNLP-CoNLL, 7:1044--1050, 2007.
K. Sagae, Y. Miyao, and J. Tsujii. HPSG parsing with shallow dependency constraints. Proceedings of the 44th Meeting of the Association for Computational Linguistics (ACL'07). Prague, Czech Republic. 2007.
K. Sagae and A. Lavie. Parser combination by reparsing. In Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pages 129--132. 2006.
K. Sagae and A. Lavie. A best-first probabilistic shift-reduce parser. In Proceedings of the COLING/ACL on Main conference poster sessions, pages 691--698. 2006.
K. Sagae, A. Lavie, and B. MacWhinney. Automatic measurement of syntactic development in child language. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 197--204. 2005.
Teaching
Linguistics 127: Text processing and corpus linguistics (Winter 2021)
Linguistics 177: Computational Linguistics (Spring 2024)
Linguistics 205: Computational models of language structure, graduate (Winter 2024)
Linguistics 201: Proseminar, graduate (Winter 2022, Spring 2022)
Linguistics 1: Introduction to Linguistics (Spring 2020)