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Information Extraction from HTML: Application of a General Machine Learning Approach (1998)
| Venue: | In Proceedings of the Fifteenth National Conference on Artificial Intelligence |
| Citations: | 175 - 6 self |
BibTeX
@INPROCEEDINGS{Freitag98informationextraction,
author = {Dayne Freitag},
title = {Information Extraction from HTML: Application of a General Machine Learning Approach},
booktitle = {In Proceedings of the Fifteenth National Conference on Artificial Intelligence},
year = {1998},
pages = {517--523}
}
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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, SRV makes no assumptions about document structure and the kinds of information available for use in learning extraction patterns. Instead, structural and other information is supplied as input in the form of an extensible token-oriented feature set. We demonstrate the effectiveness of this approach by adapting SRV for use in learning extraction rules for a domain consisting of university course and research project pages sampled from the Web. Making SRV Web-ready only involves adding several simple...
Keyphrases
information extraction general machine learning approach domain consisting extensible token-oriented feature extraction rule research project page university course general-purpose relational learner standard machine learning problem knowledge discovery document structure several simple world wide web relational learning extraction pattern
