Dunn, J. (2010). Towards a Computational Model of Metaphor. MA Thesis. Purdue University. Abstract. This thesis works towards a micro-theory of metaphor within the ontological semantics framework. It does so using a parameter-based system modeled roughly after Attardo and Raskin’s (1991) general theory of verbal humor. At the same time, it tries to convert Lakoff … More Towards a Computational Model of Metaphor
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. (2021). “Representations of Language Varieties Are Reliable Given Corpus Similarity Measures.” In Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties, and Dialects. Association for Computational Linguistics. 28-38. Abstract. This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the … More Representations of Language Varieties Are Reliable
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. Abstract. Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of … More Measuring Linguistic Diversity During COVID-19
Dunn, J. (2020). “Mapping Languages: The Corpus of Global Language Use.” Language Resources and Evaluation. 54: 999-1018. Abstract. This paper describes a web-based corpus of global language use with a focus on how this corpus can be used for data-driven language mapping. First, the corpus provides a representation of where national varieties of major languages … More Mapping Languages: The Corpus of Global Language Use
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. Abstract. While text corpora have been steadily increasing in overall size, even very large corpora are not designed to represent global population demographics. For example, recent work has … More Geographically-Balanced Gigaword Corpora for 50 Language Varieties
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. and Adams, B. (2019). “Mapping Languages and Demographics with Georeferenced Corpora.” In Proceedings of Geocomputation 2019. Abstract. This paper evaluates large georeferenced corpora, taken from both web-crawled and social media sources, against ground-truth population and language-census datasets. The goal is to determine (i) which dataset best represents population demographics; (ii) in what parts … More Mapping Languages and Demographics
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
Jonathan Dunn 