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<Paper uid="W06-3207">
  <Title>Richness of the Base and Probabilistic Unsupervised Learning in Optimality Theory</Title>
  <Section position="6" start_page="56" end_page="57" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> In sum, this paper has presented an unsupervised, probabilistic algorithm for OT learning. The paper argues that combining the OT principle of Richness of the Base and likelihood maximization provides a novel and general solution to the problem of finding a restrictive grammar. The proposed solution involves explicitly implementing Richness of the Base in the initialization of the lexicon in order to fully utilize the properties of the objective function. By relying on Richness of the Base and likelihood maximization, the algorithm is able to use negative evidence implicitly to find restrictive grammars. The algorithm is shown to be successful on three constructed languages featuring different types of neutralization and hidden structure.</Paragraph>
    <Paragraph position="1"> One potential extension of the proposed algorithm involves combining a system for unsupervised learning of morphological relations with the proposed algorithm for learning phonology. Several algorithms have been proposed for automatically inducing morphological relations, like those assumed by the present learner (Goldsmith, 2001; Snover and Brent, 2001). The task of uncovering morphological relations is complicated by allomorphic alternations that obscure the underlying identity of related morphemes. While these algorithms are very promising, their performance may be significantly enhanced if they were combined with an algorithm that models such phonological alternations.</Paragraph>
    <Paragraph position="2"> In conclusion, this is the first proposed unsupervised algorithm for OT learning that takes advan- null tage of the power of probabilistic modeling to learn a grammar and lexicon simultaneously. This paper demonstrates that combining OT theoretic principles with results from computational language learning is a worthwhile pursuit that may inform both disciplines. In this case the theoretical principle of Richness of the Base has provided a novel solution to a learning problem, but at the same time, this work also informs theoretical OT by providing a formal characterization of this theoretical principle. Future work includes testing on larger, more realistic languages, including language data with noise and variation, in order to determine the algorithm's resistance to noise and ability to model variable grammars like those observed in natural languages and in human language acquisition.</Paragraph>
  </Section>
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