LLMs Learn Constructions That Humans Do Not Know

Dunn, J. and Eida, M. (2025). “LLMs Learn Constructions That Humans Do Not Know.” In Proceedings of the Second International Workshop on Construction Grammars and NLP. Abstract. This paper investigates false positive constructions: grammatical structures which an LLM hallucinates as distinct constructions but which human introspection does not support. Both a behavioural probing task using … More LLMs Learn Constructions That Humans Do Not Know

Exploring the Constructicon

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

Exposure and Emergence in Usage-Based Grammar

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

Cognitive Linguistics Meets Computational Linguistics

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

Construction Grammars Converge Given Increased Exposure

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

Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction

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

Frequency vs. Association in Usage-Based Grammar

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

Computational Learning of Construction Grammars

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

Modeling the Complexity and Descriptive Adequacy of Construction Grammars

Dunn, J. (2018). “Modeling the Complexity and Descriptive Adequacy of Construction Grammars.” In Proceedings of the Society for Computation in Linguistics (SCiL 2018). Stroudsburg, PA: Association for Computational Linguistics. 81-90. Abstract. This paper uses the Minimum Description Length paradigm to model the complexity of CxGs (operationalized as the encoding size of a grammar) alongside their … More Modeling the Complexity and Descriptive Adequacy of Construction Grammars

Finding Variants for Construction-Based Dialectometry

Dunn, J. (2018). “Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs.” Cognitive Linguistics, 29(2): 275-311. Abstract. This paper develops a construction-based dialectometry capable of identifying previously unknown constructions and measuring the degree to which a given construction is subject to regional variation. The central idea is to learn a grammar of constructions … More Finding Variants for Construction-Based Dialectometry