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Conference Paper

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

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List,  Johann-Mattis
CALC, Max Planck Institute for the Science of Human History, Max Planck Society;

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shh2248.pdf
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Citation

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:10.17617/2.3149452.


Cite as: http://hdl.handle.net/21.11116/0000-0004-5B8D-8
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.