Empirical Methods in Information Extraction

  • Claire Cardie

Abstract

This article surveys the use of empirical, machine-learning methods for a particular natural language-understanding task-information extraction. The author presents a generic architecture for information-extraction systems and then surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture.
Published
1997-12-15
Section
Articles