Abstract

A data-oriented (empiricist) alternative to the currently pervasive (nativist) Principles and Pa-rameters approach to the acquisition of stress assignment is investigated. A similarity-based algorithm, viz. an augmented version of Instance-Based Learning is used to learn the system of main stress assignment in Dutch. In this nontrivial task a comprehensive lexicon of Dutch monomorphemes is used instead of the idealized and highly simplified description of the empirical data used in previous approaches. It is demonstrated that a similarity-based learning method is effective in learning the complex stress system of Dutch. The task is accomplished without the a priori knowledge assumed to pre-exist in the learner in a Principles and Parameters framework. A comparison of the system's behavior with a consensus linguistic analysis (in the framework of Metrical Phonology) shows that ease of learning correlates with decreasing degrees of marked-ness of metrical phenomena. It is also shown that the learning algorithm captures subregularities within the stress system of Dutch that cannot be described without going beyond some of the theoretical assumptions of metrical phonology.

Citations