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1
Materials for Pan-Romance Field Research ; Materials for Pan-Romance Field Research: Guide - Questionnaire - Worksheets
In: https://hal.archives-ouvertes.fr/hal-03609075 ; 2019 (2019)
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2
Bilingual lexical modulation using positive and negative priming within and across languages
Neumann E. - 2019
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3
Multi-unit association measures: Moving beyond pairs of words
Dunn J. - 2019
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4
What Metaphor Identification Systems Can Tell Us About Metaphor-in-Language
Dunn J. - 2019
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5
Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar
Dunn J. - : Association for Computational Linguistics, 2019
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6
Native language influence on brass instrument performance: An application of generalized additive mixed models (GAMMs) to midsagittal ultrasound images of the tongue
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7
Measuring Metaphoricity
Dunn J. - 2019
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8
Positive and negative priming between languages in two bilingual groups
Neumann E; Nkrumah I. - 2019
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9
Multi-Dimensional Abstractness in Cross-Domain Mappings
Dunn J. - 2019
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10
Modeling the Complexity and Descriptive Adequacy of Construction Grammars
Dunn J. - 2019
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11
Visual-tactile integration in speech perception : evidence for modality neutral speech primitives.
Gick B; Derrick, Donald; Bicevskis K. - : Acoustical Society of America (ASA), 2019
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12
Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology
Dunn J. - 2019
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13
Language-Independent Ensemble Approaches to Metaphor Identification
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14
How linguistic structure influences and helps to predict metaphoric meaning
Dunn J. - : Walter de Gruyter GmbH, 2019
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15
Modeling Global Syntactic Variation in English Using Dialect Classification
Dunn J. - : Association for Computational Linguistics, 2019
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16
Finding variants for construction-based dialectometry: A corpus-based approach to regional CxGs
Dunn J. - : Walter de Gruyter GmbH, 2019
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 (a CxG) using construction grammar induction and then to use these constructions as features for dialectometry. This offers a method for measuring the aggregate similarity between regional CxGs without limiting in advance the set of constructions subject to variation. The learned CxG is evaluated on how well it describes held-out test corpora while dialectometry is evaluated on how well it can model regional varieties of English. The method is tested using two distinct datasets: First, the International Corpus of English representing eight outer circle varieties; Second, a web-crawled corpus representing five inner circle varieties. Results show that the method (1) produces a grammar with stable quality across sub-sets of a single corpus that is (2) capable of distinguishing between regional varieties of English with a high degree of accuracy, thus (3) supporting dialectometric methods for measuring the similarity between varieties of English and (4) measuring the degree to which each construction is subject to regional variation. This is important for cognitive sociolinguistics because it operationalizes the idea that competition between constructions is organized at the functional level so that dialectometry needs to represent as much of the available functional space as possible.
Keyword: Communication and Culture::2004 - Linguistics; construction grammar; CxG; dialectology; dialectometry; Field of Research::20 - Language; spatial variation
URL: https://doi.org/10.1515/cog-2017-0029
http://hdl.handle.net/10092/18378
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17
Mapping Languages and Demographics with Georeferenced Corpora
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18
System for audio analysis and perception enhancement
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19
Exploring the opportunities and challenges of the digital world for early childhood services with vulnerable children
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20
A Phonological and Morphological Sketch of Isanzu Lect (Bantu, Tanzania) ; Фонетико-морфологический обзор языка исанзу (банту, Танзания)
Beletskiy, Stanislav; Diyammi, Mark Paul; Белецкий, С.. - : Сибирский федеральный университет. Siberian Federal University, 2019
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