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Phonotactic Learning With Distributional Representations, Max A. Nelson

Doctoral Dissertations

This dissertation explores the possibility that the phonological grammar manipulates phone representations based on learned distributional class memberships rather than those based on substantive linguistic features. In doing so, this work makes three primary contributions. First, I propose three novel algorithms for learning a phonological class system from the distributional statistics of a language, all of which are based on partitioning graph representations of phone distributions. Second, I propose a new method for fitting Maximum Entropy phonotactic grammars, MaxEntGrams, which offers theoretical complexity improvements over the widely-adopted approach taken by Hayes and Wilson [2008]. Third, I present a series of …