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Open Access Phylogenetic Inference from Word Lists Using Weighted Alignment with Empirically Determined Weights

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Phylogenetic Inference from Word Lists Using Weighted Alignment with Empirically Determined Weights

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The paper investigates the task of inferring a phylogenetic tree of languages from the collection of word lists made available by the Automated Similarity Judgment Project. This task involves three steps: (1) computing pairwise word distances, (2) aggregating word distances to a distance measure between languages and inferring a phylogenetic tree from these distances, and (3) evaluating the result by comparing it to expert classifications. For the first task, weighted alignment will be used, and a method to determine weights empirically will be presented. For the second task, a novel method will be developed that attempts to minimize the bias resulting from missing data. For the third task, several methods from the literature will be applied to a large collection of language samples to enable statistical testing. It will be shown that the language distance measure proposed here leads to substantially more accurate phylogenies than a method relying on unweighted Levenshtein distances between words.

Affiliations: 1: University of Tübingen gerhard.jaeger@uni-tuebingen.de

10.1163/22105832-13030204
/content/journals/10.1163/22105832-13030204
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  • Online Supporting Material
    • Publication Date : 24 March 2014
    • DOI : 10.1163/22105832-13030204_001
    • File Size: 111853
    • File format:application/pdf

The paper investigates the task of inferring a phylogenetic tree of languages from the collection of word lists made available by the Automated Similarity Judgment Project. This task involves three steps: (1) computing pairwise word distances, (2) aggregating word distances to a distance measure between languages and inferring a phylogenetic tree from these distances, and (3) evaluating the result by comparing it to expert classifications. For the first task, weighted alignment will be used, and a method to determine weights empirically will be presented. For the second task, a novel method will be developed that attempts to minimize the bias resulting from missing data. For the third task, several methods from the literature will be applied to a large collection of language samples to enable statistical testing. It will be shown that the language distance measure proposed here leads to substantially more accurate phylogenies than a method relying on unweighted Levenshtein distances between words.

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2013-01-01
2016-12-03

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