Isoglosses and Predictive Modeling

Kretzschmar, William A.

doi:10.2307/455562

Cited by 19 publications

(12 citation statements)

References 8 publications

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“…This focus is not exclusive or crisp, however. This result is in agreement with earlier studies that suggested that Kurath's delineation of dialect region is more a construct of convenience than a reality, and that the linguistic space is a continuum Kretzschmar, 1992). The results from SOM show a Northern plus Midland pattern in cluster 3 ( Figure 5.8), and a Southern plus Midland pattern in cluster 1, which addresses the northern reaches of Southern English, and especially whether and how Kurath's Midland region separates the North from the South.…”

Section: Figure 53 Component Planes Of the Node Triplets Associated supporting

confidence: 83%

Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Thill

Kretzschmar

Casas

et al. 2008

Self‐Organising Maps

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Section: Figure 53 Component Planes Of the Node Triplets Associated supporting

confidence: 83%

Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Thill

Kretzschmar

Casas

et al. 2008

Self‐Organising Maps

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“…Kretzschmar (1992b) described the rulebased process from CAs as an alternative to the use of isoglosses but his experiments were not automated. Keller (1994:100-101) reports the work of Jules Levin from an otherwise unpublished 1988 mimeograph from the University of California, Riverside.…”

Section: Definite Procedures and Cellular Automatamentioning

confidence: 99%

Creation of Regions for Dialect Features Using a Cellular Automaton

Juuso

Kretzschmar

2016

Journal of English Linguistics

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An issue in dialect research has been how to make generalizations from survey data about where some dialect feature might be found. Precomputational methods included drawing isoglosses or using shadings to indicate areas where an analyst expected a feature to be found. The use of computers allowed for faster plotting of locations where any given feature had been elicited, and also allowed for the use of statistical techniques from technical geography to estimate regions where particular features might be found. However, using the computer did not make the analysis less subjective than isoglosses, and statistical methods from technical geography have turned out to be limited in use. We have prepared a cellular automaton (CA) for use with data collected for the Linguistic Atlas Project that can address the problems involved in this type of data visualization. The CA plots the locations where survey data were elicited, and then through the application of rules creates an estimate of the spatial distributions of selected features. The application of simple rules allows the CA to create objective and reproducible estimates based on the data it was given, without the use of statistical methods.A continuous issue from the beginnings of dialect research has been how to make a reasonable generalization from survey data about where some dialect feature might be found in a survey area. Precomputational methods included drawing isoglosses or

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“…Another strand of dialectometry (Goebl 2006;Kretzschmar 1992Kretzschmar , 1996 focuses more specifically on finding dialect regions and the boundaries between them given survey-collections of known variants. These approaches force us to ask an important question: if a different set of regions had been assumed for each dataset, how consistent would the dialect model have been?…”

Section: Previous Approaches To Regions and Boundaries In Dialectometrymentioning

confidence: 99%

Finding variants for construction-based dialectometry: A corpus-based approach to regional CxGs

Dunn

2018

Cognitive Linguistics

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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.

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Isoglosses and Predictive Modeling

Cited by 19 publications

References 8 publications

Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Creation of Regions for Dialect Features Using a Cellular Automaton

Finding variants for construction-based dialectometry: A corpus-based approach to regional CxGs

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