Machine Learning of Inflection

Abstract

This chapter aims to introduce readers not familiar with computational modelling to some approaches and issues in the formal study of learnability, and the relevance of this field to theoretical linguistics and inflectional morphology in particular. After a general overview, the chapter highlights some of the obstacles in learning inflection. Inflection, considered separately from other components of language, is relatively restricted in its expressive power, which should make it easier to learn than syntax. However, inflectional systems are full of irregularities and mismatches between different levels of structure, and such irregularities make learning difficult. Overall, it is concluded that linguistically interesting proposals for machine learning of inflection should provide explanations for the nature and extent of irregularities and for the specific patterns of language acquisition and language change.