Papers By Topic

Cognitive Structures: Computational models of construction grammar and metaphor

Linguistic Variation: Computational models of spatial variation, register variation, and individual differences

Corpora: Collecting, categorizing, and validating the data we need for computational experiments

Other

(or view my papers by year here)

Cognitive Structures

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.

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.

Dunn, J. & Tayyar Madabushi, H. (2021). “Learned Construction Grammars Converge Across Registers
Given Increased Exposure.” Proceedings of the Conference on Computational Natural Language Learning (CoNLL 2021). Association for Computational Linguistics. 268-278.

Dunn, J. (2020). “Ontological and Grammatical Constraints on Metaphor Productivity.” In Attardo, S. (ed.), Script-Based Semantics Foundations and Applications. Essays in Honor of Victor Raskin. De Gruyter: 55-76. doi: 10.1515/9781501511707-004

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. doi: 10.18653/v1/W19-2913

Dunn, J. (2018). “Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words.” International Journal of Corpus Linguistics, 23(2): 183-215. doi: 10.1075/ijcl.16098.dun

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. doi: 10.7275/R59P2ZTB

Dunn, J. (2017). “Computational Learning of Construction Grammars.” Language and Cognition, 9(2): 254-292. doi: 10.1017/langcog.2016.7

Dunn, J. (2015) “Modeling Abstractness and Metaphoricity.” Metaphor & Symbol, 30(4): 259-289. doi: 10.1080/10926488.2015.1074801

Dunn, J. (2015). “Three Types of Metaphoric Utterances That Can Synthesize Theories of Metaphor.” Metaphor & Symbol, 30(1): 1-23. doi: 10.1080/10926488.2015.980694

Dunn, J. (2014). “Measuring Metaphoricity.” In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2014). Stroudsburg, PA: Association for Computational Linguistics. 745-751. doi: 10.3115/v1/P14-2121

Dunn, J. (2014). “Multi-Dimensional Abstractness in Cross-Domain Mappings.” In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Second Workshop on Metaphor in NLP (ACL 2014). Stroudsburg, PA: Association for Computational Linguistics. 27-32. doi: 10.3115/v1/W14-2304

Dunn, J.; Beltran de Heredia, J.; Burke, M.; Gandy, L.; Kanareykin, S.; Kapah, O.; Taylor, M.; Hines, D.; Frieder, O.; Grossman, D.; Howard, N.; Koppel, M.; Morris, S.; Ortony, A.; & Argamon, S. (2014). “Language-Independent Ensemble Approaches to Metaphor Identification.” In Proceedings of the 28th Conference on Artificial Intelligence: Workshop on Cognitive Computing for Augmented Human Intelligence (AAAI 2014). 6-12.

Dunn, J. (2013). “How Linguistic Structure Influences and Helps To Predict Metaphoric Meaning.” Cognitive Linguistics, 24(1): 33-66. doi: 10.1515/cog-2013-0002

Dunn, J. (2013). “Evaluating the Premises and Results of Four Metaphor Identification Systems.” In Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics, Vol. 1 (CICLING 2013). Heidelberg: Springer. 471-486. doi: 10.1007/978-3-642-37247-6_38

Dunn, J. (2013). “What Metaphor Identification Systems Can Tell Us About Metaphor-in-Language.” In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: First Workshop on Metaphor in NLP (NAACL 2013). Stroudsburg, PA: Association for Computational Linguistics. 1-10.

Dunn, J. (2011). “Gradient Semantic Intuitions of Metaphoric Expressions.” Metaphor & Symbol, 26(1): 53-67. doi: 10.1080/10926488.2011.535416

Linguistic Variation

Dunn, J. & Wong, S. (2022). “Stability of Syntactic Dialect Classification Over Space and Time.” In Proceedings of the International Conference on Computational Linguistics (COLING 2022).

Dunn, J.; Li, H.; & 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 (LREC 2022). European Language Resources Association.

Dunn, J. (2021). “Representations of Language Varieties Are Reliable Given Corpus Similarity Measures.” Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties, and Dialects (EACL 2021). Association for Computational Linguistics. 28-38.

Dunn, J. & Adams, B. (2020). “Geographically-Balanced Gigaword Corpora for 50 Language Varieties.” In Proceedings of the Language Resources and Evaluation Conference. European Language Resources Association. 2528-2536.

Dunn, J.; Coupe, T.; & Adams, B. (2020). “Measuring Linguistic Diversity During COVID-19.” Proceedings of the 4th Workshop on NLP and Computational Social Science. Association for Computational Linguistics. 1-10. http://dx.doi.org/10.18653/v1/2020.nlpcss-1.1

Dunn, J. (2019). “Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology.” In Frontiers in Artificial Intelligence: Language and Computation. doi: 10.3389/frai.2019.00015

Dunn, J. and Adams, B. (2019). “Mapping Languages and Demographics with Georeferenced Corpora.” In Proceedings of Geocomputation 2019. doi: 10.17608/k6.auckland.9869252.v2

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. doi: 10.18653/v1/W19-1405

Dunn, J. (2018). “Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs.” Cognitive Linguistics, 29(2): 275-311. doi: 10.1515/cog-2017-0029

Dunn, J; Argamon, S; Rasooli, A.; & Kumar, G. (2016) “Profile-Based Authorship Analysis.” Digital Scholarship in the Humanities, 31(4): 689-710. doi: 10.1093/llc/fqv019

Corpora

Li, H. & Dunn, J. (2022). “Corpus similarity measures remain robust across diverse languages.” Lingua, 275: 103377.

Dunn, J. & Nijhof, W. (2022). “Language Identification for Austronesian Languages.” In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association.

Dunn, J. (2020). “Mapping Languages: The Corpus of Global Language Use.” Language Resources and Evaluation. 54: 999-1018. doi: 10.1007/s10579-020-09489-2

Other

Mohammadhassan N., Mitrovic A., Neshatian K. & Dunn J. (2020). “Automatic Quality Assessment of Comments in Active Video Watching Using Machine Learning Techniques.” In So H-J; Rodrigo M; Mason J; Mitrovic A (Eds). Proceedings of the 28th International Conference on Computers in Education. I: 1-10. Taiwan: Asia-Pacific Society for Computers in Education.

Mohammadhassan N., Mitrovic A., Neshatian K. & Dunn J. (2020) “Developing Personalized Nudges to Improve Quality of Comments in Active Video Watching.” In Proceedings of 28th International Conference on Computers in Education 2: 766-769. Taiwan: Asia-Pacific Society for Computers in Education.

Graduate School

Dunn, J. (2013). Automatic Identification of Metaphoric Utterances. PhD Dissertation. Purdue University.

Dunn, J. (2010). Towards a Computational Model of Metaphor. MA Thesis. Purdue University.

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