Joint Bayesian Morphology learning for Dravidian languages

In this paper a methodology for learning the complex agglutinative morphology of some Indian languages using Adaptor Grammars and morphology rules is presented. Adaptor grammars are a compositional Bayesian framework for grammatical inference, where we define a morphological grammar for agglutinative languages and morphological boundaries are inferred from a plain text corpus. Once morphological segmentations are produce, regular expressions for sandhi rules and orthography are applied to achieve the final segmentation. We test our algorithm in the case of two complex languages from the Dravidian family. The same morphological model and results are evaluated comparing to other state-of-the art unsupervised morphology learning systems.