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Capturing diversity in language acquisition research


Stoll, Sabine; Bickel, Balthasar (2013). Capturing diversity in language acquisition research. In: Bickel, Balthasar; Grenoble, Lenore A; Peterson, David A; Timberlake, Alan. Language Typology and Historical Contingency. Amsterdam: John Benjamins Publishing Co., 195 - 216.

Abstract

In order to understand how children cope with the enormous variation in structures worldwide, developmental paths need to be studied in a sufficiently varied sample of languages. Because each study requires very large and expensive longitudinal corpora (about one million words, five to seven years of development), the relevant sample must be chosen strategically. We propose to base the choice on the results of a clustering algorithm (fuzzy clustering) applied to typological databases. The algorithm establishes a sample that maximizes the typological differences between languages. As a case study, we apply the algorithm to a dozen typological variables known to have an impact on acquisition, concerning such issues as the presence and nature of agreement and case marking, word order, degrees of synthesis, polyexponence and inflectional compactness of categories, syncretism, the existence of inflectional classes etc. The results allow deriving small samples that are maximally diverse. As a side result, we also note that while the clustering algorithm allows maximization of diversity for sampling purposes, the resulting clusters themselves are far from being discrete and therefore do not reflect a natural partition into basic language types.

Abstract

In order to understand how children cope with the enormous variation in structures worldwide, developmental paths need to be studied in a sufficiently varied sample of languages. Because each study requires very large and expensive longitudinal corpora (about one million words, five to seven years of development), the relevant sample must be chosen strategically. We propose to base the choice on the results of a clustering algorithm (fuzzy clustering) applied to typological databases. The algorithm establishes a sample that maximizes the typological differences between languages. As a case study, we apply the algorithm to a dozen typological variables known to have an impact on acquisition, concerning such issues as the presence and nature of agreement and case marking, word order, degrees of synthesis, polyexponence and inflectional compactness of categories, syncretism, the existence of inflectional classes etc. The results allow deriving small samples that are maximally diverse. As a side result, we also note that while the clustering algorithm allows maximization of diversity for sampling purposes, the resulting clusters themselves are far from being discrete and therefore do not reflect a natural partition into basic language types.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Department of Comparative Linguistics
Dewey Decimal Classification:490 Other languages
890 Other literatures
410 Linguistics
Language:English
Date:2013
Deposited On:23 Dec 2013 11:52
Last Modified:08 Dec 2017 01:16
Publisher:John Benjamins Publishing Co.
Series Name:Typological Studies in Language
Number:104
ISSN:0167-7373
ISBN:978-90-272-0685-5
Official URL:https://benjamins.com/#catalog/books/tsl.104.08slo/details
Related URLs:http://www.jbe-platform.com/content/books/9789027270801
http://opac.nebis.ch/F/?local_base=NEBIS&CON_LNG=GER&func=find-b&find_code=SYS&request=010032926

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