Contact Information
707 S Mathews Avenue
M/C 168
Urbana, IL 61801
Additional Campus Affiliations
Associate Professor, Linguistics
External Links
I am a computational linguist. My research models both (i) the emergence of grammatical structure within individuals and (ii) variation in grammatical structure across populations and registers.
To support this research, I've worked to develop large multi-lingual geographic corpora. My recent work has also focused on the impact of linguistic variation on natural language processing and on low-resource contexts. I have published over 40 papers on computational linguistics and two monographs with Cambridge University Press: Computational Construction Grammar (2024) and Natural Language Processing for Corpus Linguistics (2022). My interdisciplinary teaching experience includes a MOOC which has now taught 14,000 students about NLP.
If you are a student interested in studying computational linguistics at Illinois, feel free to send me an email!
Highlighted Publications
Dunn, J. E. (2024). Computational Construction Grammar: A Usage-Based Approach. (Elements in Cognitive Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009233743
Dunn, J. (2022). Natural Language Processing for Corpus Linguistics. (Elements in Corpus Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009070447
Dunn, J. E. (2023). Syntactic variation across the grammar: Modelling a complex adaptive system. Frontiers in Complex Systems, 1, Article 1273741. https://doi.org/10.3389/fcpxs.2023.1273741
Recent Publications
Dunn, J. E. (2024). Computational Construction Grammar: A Usage-Based Approach. (Elements in Cognitive Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009233743
Dunn, J. E., & Edwards-Brown, L. (2024). Geographically-Informed Language Identification. In Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Dunn, J., & Edwards-Brown, L. (2024). Geographically-Informed Language Identification. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 7672-7682). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). European Language Resources Association (ELRA).
Dunn, J. E., Adams, B., & Madabushi, H. T. (2024). Pre-Trained Language Models Represent Some Geographic Populations Better Than Others. In Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Dunn, J., Adams, B., & Madabushi, H. T. (2024). Pre-Trained Language Models Represent Some Geographic Populations Better Than Others. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 12966-12976). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). European Language Resources Association (ELRA).