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<Paper uid="W06-3203">
  <Title>Learning Quantity Insensitive Stress Systems via Local Inference</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> 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 neighborhooddistinctness, 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.</Paragraph>
  </Section>
class="xml-element"></Paper>