Dunn, J. (2023). “Exploring the Constructicon: Linguistic Analysis of a Computational CxG.” In Proceedings of the Workshop on CxGs and NLP @ the Georgetown University Round Table on Linguistics / SyntaxFest. Association for Computational Linguistics. Abstract. Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. … More Exploring the Constructicon
Dunn, J. (2022). “Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages.” Cognitive Linguistics. 33(4): 659-699. Abstract. This paper uses computational experiments to explore the role of exposure in the emergence of construction grammars. While usage-based grammars are hypothesized to depend on a learner’s exposure to actual language use, the mechanisms of such … More Exposure and Emergence in Usage-Based Grammar
Dunn, J. and Wong, S. (2022). “Stability of Syntactic Dialect Classification Over Space and Time.” In Proceedings of International Conference on Computational Linguistics (COLING 2022). 26-36. Abstract. This paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of … More Stability of Syntactic Dialect Classification Over Space and Time
Dunn, J. (2022). “Cognitive Linguistics Meets Computational Linguistics: Construction Grammar, Dialectology, and Linguistic Diversity.” In Tay, D. & Xie Pan, M. (eds.), Data Analytics in Cognitive Linguistics: Methods and Insights. 273-308. Berlin: De Gruyter. Abstract. Computational linguistics and cognitive linguistics come together when we use data-driven methods to conduct linguistic experiments on corpora. This chapter … More Cognitive Linguistics Meets Computational Linguistics
Dunn, J. & Tayyar Madabushi, H. (2021). “Learned Construction Grammars Converge Across RegistersGiven Increased Exposure.” Proceedings of the Conference on Computational Natural Language Learning (CoNLL 2021). Association for Computational Linguistics. Abstract. This paper measures the impact of increased exposure on whether learned construction grammars converge onto shared representations when trained on data from different registers. … More Construction Grammars Converge Given Increased Exposure
Dunn, J. & Nini, A. (2021). “Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction.” Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (NAACL 2021). Association for Computational Linguistics. 149-159. Abstract. This paper asks whether a distinction between production-based and perception-based grammar induction influences either (i) the growth curve of grammars … More Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction
Dunn, J. (2019). “Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology.” In Frontiers in Artificial Intelligence: Language and Computation. Abstract. The goal of this paper is to provide a complete representation of regional linguistic variation on a global scale. To this end, the paper focuses on removing three constraints that have previously limited … More Global Syntactic Variation in Seven Languages
Dunn, J. (2019). “Modeling Global Syntactic Variation in English Using Dialect Classification.” In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects (NAACL 19). Association for Computational Linguistics. 42-53. Abstract. This paper evaluates global-scale dialect identification for 14 national varieties of English as a means for studying syntactic variation. The paper … More Modeling Global Syntactic Variation in English
Dunn, J. (2019). “Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar.” In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (NAACL 19). Association for Computational Linguistics. 117-128. Abstract. A usage-based Construction Grammar (CxG) posits that slot-constraints generalize from common exemplar constructions. But what is the best model of constraint generalization? This … More Frequency vs. Association in Usage-Based Grammar
Dunn, J. (2017). “Computational Learning of Construction Grammars.” Language and Cognition, 9(2): 254-292. Abstract. This paper presents an algorithm for learning the construction grammar of a language from a large corpus. This grammar induction algorithm has two goals: first, to show that construction grammars are learnable without highly specified innate structure; second, to develop a … More Computational Learning of Construction Grammars
Dr. Jonathan Dunn 