Learning Quantity Insensitive Stress Systems via Local Inference

This paper presents an unsupervised batch learner for the quantity-insensitive stress systems described in Gordon (2002). Unlike previous stress learning models, the learner presented here is neither cue based (Dresher and Kaye, 1990), nor reliant on a priori Optimality-theoretic constraints (Tesar, 1998). Instead our learner exploits a property called neighborhood-distinctness, which is shared by all of the target patterns. Some consequences of this approach include a natural explanation for the occurrence of binary and ternary rhythmic patterns, the lack of higher n-ary rhythms, and the fact that, in these systems, stress always falls within a certain window of word edges.