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    Chapter Black box approaches to genealogical classification and their shortcomings

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    Author(s)
    Prokić, Jelena
    Moran, Steven
    Contributor(s)
    Saxena, Anju (editor)
    Borin, Lars (editor)
    Collection
    European Research Council (ERC); EU collection
    Language
    English
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    Abstract
    In the past 20 years, the application of quantitative methods in historical linguistics has received a lot of attention. Traditional historical linguistics relies on the comparative method in order to determine the genealogical related-ness of languages. More recent quantitative approaches attempt to automate this process, either by developing computational tools that complement the comparative method (Steiner et al. 2010) or by applying fully automatized methods that take into account very limited or no linguistic knowledge, e.g. the Levenshtein approach. The Levenshtein method has been extensively used in dialectometry to measure the distances between various dialects (Kessler 1995; Heeringa 2004; Nerbonne 1996). It has also been frequently used to analyze the relatedness between languages, such as Indo-European (Serva and Petroni 2008; Blanchard et al. 2010), Austronesian (Petroni and Serva 2008), and a very large sample of 3002 languages (Holman 2010). In this paper we will examine the performance of the Levenshtein distance against n-gram models and a zipping approach by applying these methods to the same set of language data.
    Book
    Approaches to Measuring Linguistic Differences
    URI
    http://library.oapen.org/handle/20.500.12657/23715
    Keywords
    linguistic differences
    DOI
    10.1515/9783110305258.429
    ISBN
    9783110488081
    OCN
    1135845144
    Publisher
    De Gruyter
    Publisher website
    https://www.degruyter.com/
    Publication date and place
    Berlin/Boston, 2013
    Grantor
    • FP7 Ideas: European Research Council - 240816 - QUANTHISTLING Research grant informationFind all documents
    Classification
    Linguistics
    Rights
    All rights reserved
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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