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Cognacy and computational cladistics: Issues in determining lexical cognacy for Indo-European cladistic research

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Scarborough,  Matthew
Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society;

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Citation

Scarborough, M. (2019). Cognacy and computational cladistics: Issues in determining lexical cognacy for Indo-European cladistic research. In M. Serangeli, & T. Olander (Eds.), Dispersals and diversification: Linguistic and archaeological perspectives on the early stages of Indo-European (pp. 54-76). Leiden: Brill. doi:10.1163/9789004416192_011.


Cite as: http://hdl.handle.net/21.11116/0000-0007-8F57-7
Abstract
Cladistic hypotheses are ideally based on arguments that use cumulative evidence from a wide range of shared innovations inherited from a more recent ancestor. The majority of historical linguists would agree that the best evidence for subgrouping would be shared phonological and morphological innovations, while evidence from proportions of shared lexical cognacy is less reliable for linguistic subgrouping. Recent high-profile studies have appeared, however, that have been based exclusively on comparative lexical material. The results of these methods have been sharply criticised, but in spite of the criticisms to cognacy-based approaches, there remains some potential that the lexical cognacy may provide some useful data to supplement cladistic hypotheses as part of an overall assessment of the complete bundle of available isoglosses. If lexical cognacy judgements can be treated as a potential source of data for cladistic hypotheses, how can they be implemented in a methodologically rigorous way? This chapter focuses on case studies from methodological issues that have arisen in encoding Indo-European lexical cognacy data on the Indo-European Cognate Relationships (IE-CoR) database project based at the Max Planck Institute for the Science of Human History. These issues are illustrated through case studies from problems that have arisen in assigning cognacy coding to lexical data. As such this chapter contributes a discussion towards improving the reliability of cognacy data for cladistic analyses as a supplement to more traditional analyses based on comparative phonological and morphological criteria.