Learning agglutinative morphology of indian languages with linguistically motivated adaptor grammars

Abstract

In this paper an automatic morphology learning system for complex and agglutinative languages is presented. We process complex agglutinative morphology of Indian languages using Adaptor Grammars and linguistic rules of morphology. Adaptor Grammars are a compositional Bayesian framework for grammatical inference, where we define a 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.