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