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1
Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs ...
Dunn, Jonathan. - : arXiv, 2021
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2
Representations of Language Varieties Are Reliable Given Corpus Similarity Measures ...
Dunn, Jonathan. - : arXiv, 2021
BASE
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3
Measuring Linguistic Diversity During COVID-19 ...
BASE
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4
Learned Construction Grammars Converge Across Registers Given Increased Exposure ...
BASE
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5
Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction ...
Dunn, Jonathan; Nini, Andrea. - : arXiv, 2021
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6
Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology ...
Dunn, Jonathan. - : arXiv, 2021
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7
Natural Language Processing for Corpus Linguistics ...
Dunn, Jonathan. - : Code Ocean, 2021
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8
Learned Construction Grammars Converge Across Registers Given Increased Exposure
Dunn, Jonathan; Tayyar Madabushi H. - : Association for Computational Linguistics, 2021
BASE
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9
Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction
Nini A; Dunn, Jonathan. - : Association for Computational Linguistics, 2021
BASE
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10
Representations of Language Varieties Are Reliable Given Corpus Similarity Measures
Dunn, Jonathan. - : Association for Computational Linguistics, 2021
BASE
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11
Mapping languages: the Corpus of Global Language Use [<Journal>]
Dunn, Jonathan [Verfasser]
DNB Subject Category Language
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12
Modeling Global Syntactic Variation in English Using Dialect Classification ...
Dunn, Jonathan. - : arXiv, 2019
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13
Mapping Languages and Demographics with Georeferenced Corpora
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14
Global Syntactic Variation in Seven Languages: Toward a Computational Dialectology
In: Front Artif Intell (2019)
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 work within dialectology/dialectometry. First, rather than assuming a fixed and incomplete set of variants, we use Computational Construction Grammar to provide a replicable and falsifiable set of syntactic features. Second, rather than assuming a specific area of interest, we use global language mapping based on web-crawled and social media datasets to determine the selection of national varieties. Third, rather than looking at a single language in isolation, we model seven major languages together using the same methods: Arabic, English, French, German, Portuguese, Russian, and Spanish. Results show that models for each language are able to robustly predict the region-of-origin of held-out samples better using Construction Grammars than using simpler syntactic features. These global-scale experiments are used to argue that new methods in computational sociolinguistics are able to provide more generalized models of regional variation that are essential for understanding language variation and change at scale.
Keyword: Artificial Intelligence
URL: https://doi.org/10.3389/frai.2019.00015
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861279/
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15
Recursively emerging structure: a discourse-device CxG
In: Proceedings of the fifty-third (53.) annual meeting of the Chicago Linguistic Society (2018), S. 71-85
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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16
Finding variants for construction-based dialectometry: a corpus-based approach to regional CxGs
In: Cognitive linguistics. - Berlin ; Boston, Mass. : de Gruyter Mouton 29 (2018) 2, 275-311
BLLDB
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17
Modeling the Complexity and Descriptive Adequacy of Construction Grammars
In: Proceedings of the Society for Computation in Linguistics (2018)
BASE
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18
Learnability and falsifiability of Construction Grammars
In: Proceedings of the Linguistic Society of America; Vol 2 (2017): Proceedings of the Linguistic Society of America; 1:1–15 ; 2473-8689 (2017)
BASE
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19
The Linguistic Status of Predictions and Feature Ranks from SVM Text Classifiers
In: LSA Annual Meeting Extended Abstracts; Vol 6: LSA Annual Meeting Extended Abstracts 2015; 5:1-5 ; 2377-3367 (2015)
BASE
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20
Book review
In: Cognitive linguistics. - Berlin ; Boston, Mass. : de Gruyter Mouton 24 (2013) 4, 711-717
OLC Linguistik
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