five

Computational phylogenetics reveal the history of sign languages

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NIAID Data Ecosystem2026-05-01 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.bnzs7h4h1
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Sign languages are naturally occurring languages. As such, their emergence and spread reflect the histories of their communities. However, limitations in historical recordkeeping and linguistic documentation have hindered diachronic analysis of sign languages. Here, we use computational phylogenetic methods to study family structure among 19 sign languages from deaf communities worldwide. We use phonologically coded lexical data from contemporary languages to infer relatedness, and suggest these methods can help study regular form changes in sign languages. The inferred trees are consistent in key respects with known historical information, but challenge certain assumed groupings and surpass analyses made available by traditional methods. Moreover, the phylogenetic inferences are not reducible to geographic distribution, but do affirm the importance of geopolitical forces in the histories of human languages. Methods Data set comprises coded vocabulary data from 19 sign languages. Vocabulary items were sourced from freely available online sign language dictionaries and were annotated using a web-based interface developed for the project. The categories and category values used in the coding system are compatible with and informed by leading contemporary theories of sign language phonology. Additional information about data collection and coding is available in Section 2 and Section 4 of the supplementary materials text.
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2024-02-05
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