Learning agglutinative morphology of Indian languages with linguistically motivated adaptors grammars

Abstract

In this paper an automatic mprphology learning system for complex and agglutinative morphology of Indian Languages using Adaptor Grammars and linguistic rules of mophology. 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 corpora of plain text. Once it produces morphological segmentation, regular expressions for orthography rules are applied to achieve final segmentation. We test our algorithm in the case of three complex languages from the Dravidian family and evaluate the results comparing to other state of the art unsupervised morphology learning systems and show significant improvements in the results.