Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Discovery of Phonological Categories through Supervised
  Learning of Morphological Rules   We describe a case study in the application of {\em symbolic machine
learning} techniques for the discovery of linguistic rules and categories. A
supervised rule induction algorithm is used to learn to predict the correct
diminutive suffix given the phonological representation of Dutch nouns. The
system produces rules which are comparable to rules proposed by linguists.
Furthermore, in the process of learning this morphological task, the phonemes
used are grouped into phonologically relevant categories. We discuss the
relevance of our method for linguistics and language technology.

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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci