Empirical Methods in Information Extraction

This article surveys the use of empirical, machinelearning 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. Author Eugene Charniak and coauthors Ng Hwee Tou and John Zelle, for example, describe techniques for part-of-speech tagging, parsing, and word-sense disambiguation. These techniques were created with no specific domain or high-level language-processing task in mind. In contrast, my article surveys the use of empirical methods for a particular natural language-understanding task that is inherently domain specific.