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.

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DOI: http://dx.doi.org/10.1609/aimag.v18i4.1322

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