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

Authors:
Wenhui Liao;Marc Light;Sriharsha Veeramachaneni
Affiliations:
Research and Development, Thomson Reuters, Eagan, MN;Research and Development, Thomson Reuters, Eagan, MN;Research and Development, Thomson Reuters, Eagan, MN
Venue:
SETQA-NLP '09 Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing
Year:
2009

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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.