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
How to Cite
Cardie, C. (1997). Empirical Methods in Information Extraction. AI Magazine, 18(4), 65. https://doi.org/10.1609/aimag.v18i4.1322
Section
Articles