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  An automated framework for fast cognate detection and bayesian phylogenetic inference in computational historical linguistics

Rama, T., & List, J.-M. (2019). An automated framework for fast cognate detection and bayesian phylogenetic inference in computational historical linguistics. In Proceedings of the 57th Conference of the Association for Computational Linguistics (pp. 6225-6235). Florence: Association for Computational Linguistics. doi:10.18653/v1/P19-1627.

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shh2248.pdf (Publisher version), 189KB
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 Creators:
Rama, Taraka, Author
List, Johann-Mattis1, Author           
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1CALC, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2385703              

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 Abstract: We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.

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Language(s): eng - English
 Dates: 2019-072019-07
 Publication Status: Issued
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.18653/v1/P19-1627
DOI: 10.17617/2.3149452
Other: shh2248
DOI: 10.5281/zenodo.3237508
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Title: Proceedings of the 57th Conference of the Association for Computational Linguistics
Source Genre: Proceedings
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Publ. Info: Florence : Association for Computational Linguistics
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 6225 - 6235 Identifier: -