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Abstract: Because the World Wide Web consists primarily of text, information extraction is central to any effort that would use the Web as a resource for knowledge discovery. We show how information extraction can be cast as a standard machine learning problem, and argue for the suitability of relational learning in solving it. The implementation of a general-purpose relational learner for information extraction, SRV, is described. In contrast with earlier learning systems for information extraction,... (Update)
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BibTeX entry: (Update)
Freitag, D. Information extraction from html: Application of a general learning approach. Proceedings of the Fifteenth Conference on Artificial Intelligence AAAI-98 (1998), 517--523. http://citeseer.ist.psu.edu/freitag98information.html More
@inproceedings{ freitag98information,
author = "Dayne Freitag",
title = "Information Extraction from {HTML}: Application of a General Machine Learning Approach",
booktitle = "{AAAI}/{IAAI}",
pages = "517-523",
year = "1998",
url = "citeseer.ist.psu.edu/freitag98information.html" }
Citations (may not include all citations):492 Learning logical definitions from relations (context) - Quinlan - 1990 ACM DBLP
228 Wrapper Induction for Information Extraction - Kushmerick - 1997 ACM DBLP
180 The CN2 induction algorithm (context) - Clark, Niblett - 1989 ACM DBLP
105 Estimating probabilities: A crucial task in machine learning (context) - Cestnik - 1990 DBLP
70 Relational learning of pattern-match rules for information e.. - Califf, Mooney - 1997 ACM DBLP
18 Learning Text Analysis Rules for Domain-specific Natural Lan.. - Soderland - 1996 ACM
3 Empirical methods in information extraction (context) - Papers, ACL- et al. - 1997 DBLP
2 Learning to extract text-based information from the world wi.. (context) - University, CS et al. - 1997 DBLP
The graph only includes citing articles where the year of publication is known.
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