Abstract
A probabilistic version of word distributional equivalence, which includes the usual notion of syntactic distributional classes as a special case, is formulated. A computational procedure for the unsupervised discovery of probabilistic distributional classes, using random text presentation, is shown to converge stochastically to the correct classification. The results of a simulation experiment are presented. A geometrical interpretation of the procedure, in which words are represented as vectors in an infinite-dimensional inner product space, is discussed.
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References
S. Shrier, “Abduction algorithms for grammar discovery,” Division of Applied Mathematics, Brown University, Providence, R.I. (1977).
S. Marcus,Algebraic Linguistics: Analytical Models (Academic Press, New York, 1967).
U. Grenander,Lectures in Pattern Theory, vol. 1 (Springer-Verlag, Berlin, 1978).
M. G. Kendall and A. Stuart,The Advanced Theory of Statistics, vol. 1, 4th ed. (Griffin, London, 1977).
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Brody, E.J. A procedure for the unsupervised abdunction of linguistic distributional classes in probabilistic languages. International Journal of Computer and Information Sciences 11, 193–210 (1982). https://doi.org/10.1007/BF00999728
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DOI: https://doi.org/10.1007/BF00999728