Stochastic Error-Correcting Syntax Analysis for Recognition of Noisy Patterns.

摘要

In this paper, a probabilistic model for error-correcting parsing with substitution, insertion, and deletion errors is introduced. The formulation of maximum-likelihood error-correcting parser (MLECP) by incorporating the noise model into stochastic grammars is also presented. The use of stochastic error-correcting parsers for recognition of noisy and/or distorted patterns results in a process of high accuracy, but with low efficiency. In order to make the syntax analysis more practically feasible, it is proposed to use a sequential classification method for noisy strings processing. Computation results based on the classification experiments of noisy patterns for both nonsequential and sequential error-correcting parsers are presented. (Author)