Jonathan Dunn

Senior LecturerJonathan Dunn

Elsie Locke Building 206
Internal Phone: 90305
How can computational linguistics inform linguistic theory?

Qualifications

Research Interests

Jonathan is a computational linguist working on models of language learning and language change using large multi-lingual corpora. The underlying challenge is to model how speakers converge onto similar grammars while at the same time modelling how grammars diverge into distinct dialects and registers.

Before joining the University of Canterbury, Jonathan held positions in computer science at the Illinois Institute of Technology and received a PhD in linguistics from Purdue University under Victor Raskin. He has published over 30 papers in computational linguistics and his first book was recently published by Cambridge University Press.

Recent Publications

  • Dunn J. (2022) Natural Language Processing for Corpus Linguistics. Cambridge University Press. http://dx.doi.org/10.1017/9781009070447.
  • Dunn J. (2022) Cognitive Linguistics Meets Computational Linguistics: Construction Grammar, Dialectology, and Linguistic Diversity.. In Tay D; Pan MX (Ed.), Data Analytics in Cognitive Linguistics: Methods and Insights: 273-308.De Gruyter Mouton. http://dx.doi.org/10.1515/9783110687279-010.
  • Dunn J. and Nijhof W. (2022) Language Identification for Austronesian Languages. In Proceedings of the 13th International Conference on Language Resources and Evaluation: 6530-6539. European Language Resources Association.
  • Dunn J., Li H. and Sastre D. (2022) Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures. In Proceedings of the 13th International Conference on Language Resources and Evaluation: 6461-6470. European Language Resources Association.
  • Li H. and Dunn J. (2022) Corpus similarity measures remain robust across diverse languages. Lingua http://dx.doi.org/10.1016/j.lingua.2022.103377.