A multi-purpose bayesian model for word-based morphology

Abstract

This paper introduces a probabilistic model of morphology based on a word-based morphological theory. Morphology is understood here as a system of rules that describe systematic correspondences between full word forms, without decomposing words into any smaller units. The model is formulated in the Bayesian learning framework and can be trained in both supervised and unsupervised setting. Evaluation is performed on tasks of generating unseen words, lemmatization and inflected form production.