Studies in computational historical linguistics: Models and analyses

Abstract

Computational analysis of historical and typological data has made great progress in the last fifteen years. In this thesis, I work with vocabulary lists for addressing some classical problems in historical linguistics such as cognate identification, discriminating related languages from unrelated languages, assigning possible dates to splits in a language family, and providing an internal structure to a language family. I compare the internal structure inferred from vocabulary lists with the family trees given in Ethnologue. I explore the ranking of lexical items in the widely used Swadesh word list and compare my ranking to another quantitative reranking method and short word lists composed for discovering long-distance genetic relationships. I show that the choice of string similarity measures is important for internal classification and for discriminating related from unrelated languages. The dating system presented in this thesis can be used for assigning age estimates to any new language group and overcomes the assumption of a constant rate of lexical replacement assumed by glottochronology. I train and test a linear classifier based on gap-weighted subsequence features for the purpose of cognate identification. An important conclusion from these results is that n-gram approaches can be used for different historical linguistic purposes.