Constraints, Exceptions and Representations

TL;DR

This paper demonstrates that default-based phonologies can better capture certain morphophonological generalizations than non-default theories, highlighting limitations of machine learning approaches for analyzing such phenomena.

Contribution

It offers a characterization of Underspecification Theory and Optimality Theory based on their default ordering methods, advancing theoretical understanding.

Findings

Default-based phonologies capture unique morphophonological generalizations.

Machine learning techniques may not be suitable for non-default morphophonological analysis.

The paper provides a formal characterization of key phonological theories.

Abstract

This paper shows that default-based phonologies have the potential to capture morphophonological generalisations which cannot be captured by non-defaul theories. In achieving this result, I offer a characterisation of Underspecification Theory and Optimality Theory in terms of their methods for ordering defaults. The result means that machine learning techniques for building non-defualt analyses may not provide a suitable basis for morphophonological analysis.