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Abstract: Information extraction systems process natural language documents and locate a specific set of relevant items. Given the recent success of empirical or corpusbased approaches in other areas of natural language processing, machine learning has the potential to significantly aid the development of these knowledge-intensive systems. This paper presents a system, Rapier, that takes pairs of documents and filled templates and induces pattern-match rules that directly extract fillers for the slots... (Update)
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BibTeX entry: (Update)
Califf, M. E., and Mooney, R. J. 1998. Relational learning of pattern-match rules for information extraction. http://citeseer.ist.psu.edu/article/califf97relational.html More
@inproceedings{ califf98relational,
author = "M. E. Califf and R. J. Mooney",
title = "Relational Learning of Pattern-Match Rules for Information Extraction",
booktitle = "Working Notes of {AAAI} Spring Symposium on Applying Machine Learning to Discourse Processing",
publisher = "AAAI Press",
address = "Menlo Park, CA",
pages = "6--11",
year = "1998",
url = "citeseer.ist.psu.edu/article/califf97relational.html" }
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