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Automatic cognate classification with a Support Vector Machine

Most current approaches in computational phylogenetic linguistics require as input multilingual word lists that are categorized into cognate classes. Cognate classification is currently usually done manually by experts, which is time consuming and so far only available for a small number of well-studied language families. Automatizing this step will greatly expand the empirical scope of phylogenetic methods in linguistics, as raw word lists (in phonetic transcription) are much easier to obtain than cognate-coded ones, especially for under-studied language families. Here we propose a method for automatic cognate classification using supervised learning with a Support Vector Machine. The method outperforms JohannMattis List’s SCA and LexStat methods (List, 2012; List, 2014b), the current de facto standard.