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Abstract: There is a wealth of information to be mined from narrative text on the World Wide Web. Unfortunately, standard natural language processing (NLP) extraction techniques expect full, grammatical sentences, and perform poorly on the choppy sentence fragments that are often found on web pages. This paper 1 introduces Webfoot, a preprocessor that parses web pages into logically coherent segments based on page layout cues. Output from Webfoot is then passed on to CRYSTAL, an NLP system that learns... (Update)
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BibTeX entry: (Update)
Stephen Soderland. Learning to extract text-based information from the world wide web. In Proceedings of Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997. http://citeseer.ist.psu.edu/soderland97learning.html More
@inproceedings{ soderland97learning,
author = "Stephen Soderland",
title = "Learning to Extract Text-Based Information from the World Wide Web",
booktitle = "Knowledge Discovery and Data Mining",
pages = "251-254",
year = "1997",
url = "citeseer.ist.psu.edu/soderland97learning.html" }
Citations (may not include all citations):392 A Theory and Methodology of Inductive Learning (context) - Michalski - 1983
233 The CN2 Induction Algorithm - Clark, Niblett - 1989
228 Wrapper Induction for Information Extraction - Kushmerick, Weld et al. - 1997
171 A Scalable Comparison-Shopping Agent for the World-Wide Web - Doorenbos, Etzioni et al. - 1997
79 CRYSTAL: Inducing a Conceptual Dictionary - Soderland, Fisher et al. - 1995
19 The NYU System for MUC-6 or Where's the Syntax - Grishman - 1995
9 BBN: Description of the PLUM System as Used for MUC (context) - Weischedel - 1995
7 SRA: Description of the SRA System as Used for MUC (context) - Krupka - 1995
The graph only includes citing articles where the year of publication is known.
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