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Integrating rules and statistical systems is a challenge often faced by natural language processing system builders. A common subclass is integrating high precision rules with a Markov statistical sequence classifier. In this paper we suggest that using such rules to constrain the sequence classifier decoder results in superior accuracy and efficiency. In a case study of a named entity tagging system, we provide evidence that this method of combination does prove efficient than other methods. The accuracy was the same.

Integrating high precision rules with statistical sequence classifiers for accuracy and speed