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<p>The authors describe an effort to adapt island-driven parsers to handle stochastic context-free grammars. These grammars could be used as language models (LMs) by a language processor (LP) to computer the probability of a linguistic interpretation. As different islands may compete for growth, it is important to compute the probability that an LM generates a sentence containing islands and gaps between them. Algorithms for computing these probabilities are introduced. The complexity of these algorithms is analyzed both from theoretical and practical points of view. It is shown that the computation of probabilities in the presence of gaps of unknown length requires the impractical solution of a nonlinear system of equations, whereas the computation of probabilities for cases with gaps containing a known number of unknown words has polynomial time complexity and is practically feasible. The use of the results obtained in automatic speech understanding systems is discussed.</p>
island-driven parser; stochastic context-free grammars; language models; probability; linguistic interpretation; polynomial time complexity; automatic speech understanding; artificial intelligence; computational complexity; context-free grammars; linguistics; natural languages; speech recognition

A. Corraza, R. Gretter, R. De Mori and G. Sata, "Computation of Probabilities for an Island-Driven Parser," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 936-950, 1991.
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