Integrating High Precision Rules with Statistical Sequence Classifiers for Accuracy and Speed

Abstract

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.