Empirical Methods in Information Extraction (1997)
| Venue: | AI magazine |
| Citations: | 92 - 7 self |
BibTeX
@ARTICLE{Cardie97empiricalmethods,
author = {Claire Cardie},
title = {Empirical Methods in Information Extraction},
journal = {AI magazine},
year = {1997},
volume = {18},
pages = {65--79}
}
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Abstract
this article surveys the use of empirical methods for a particular natural language understanding task that is inherently domain-specific. The task is information extraction. Very generally, an information extraction system takes as input an unrestricted text and "summarizes" the text with respect to a prespecified topic or domain of interest: it finds useful information about the domain and encodes that information in a structured form, suitable for populating databases. In contrast to in-depth natural language understanding tasks, information extraction systems effectively skim a text to find relevant sections and then focus only on these sections in subsequent processing. The information extraction system in Figure 1, for example, summarizes stories about natural disasters, extracting for each such event the type of disaster, the date and time that it occurred, and data on any property damage or human injury caused by the event. Infor
