Probabilistic, Information-Theoretic Models for Etymological Alignment

Hannes Wettig


This thesis starts out by reviewing Bayesian reasoning and Bayesian network models. We present results related to discriminative learning of Bayesian network parameters. Along the way, we explicitly identify a number of problems arising in Bayesian model class selection. This leads us to information theory and, more specically, the minimum description length (MDL) principle. We look at its theoretic foundations and practical implications. The MDL approach provides elegant solutions for the problem of model class selection and enables us to objectively compare any set of models, regardless of their parametric structure. Finally, we apply these methods to problems arising in computational etymology. We develop model families for the task of sound-by-sound alignment across kindred languages. Fed with linguistic data in the form of cognate sets, our methods provide information about the correspondence of sounds, as well as the history and ancestral structure of a language family. As a running example we take the family of Uralic languages.