Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Unsupervised information extraction from unstructured, ungrammatical data sources on the World Wide Web

Unsupervised information extraction from unstructured, ungrammatical data sources on the World... Information extraction from unstructured, ungrammatical data such as classified listings is difficult because traditional structural and grammatical extraction methods do not apply. Previous work has exploited reference sets to aid such extraction, but it did so using supervised machine learning. In this paper, we present an unsupervised approach that both selects the relevant reference set(s) automatically and then uses it for unsupervised extraction. We validate our approach with experimental results that show our unsupervised extraction is competitive with supervised machine learning approaches, including the previous supervised approach that exploits reference sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Document Analysis and Recognition (IJDAR) Springer Journals

Unsupervised information extraction from unstructured, ungrammatical data sources on the World Wide Web

Loading next page...
 
/lp/springer-journals/unsupervised-information-extraction-from-unstructured-ungrammatical-RlWuu8dwy7

References (25)

Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision
ISSN
1433-2833
eISSN
1433-2825
DOI
10.1007/s10032-007-0052-2
Publisher site
See Article on Publisher Site

Abstract

Information extraction from unstructured, ungrammatical data such as classified listings is difficult because traditional structural and grammatical extraction methods do not apply. Previous work has exploited reference sets to aid such extraction, but it did so using supervised machine learning. In this paper, we present an unsupervised approach that both selects the relevant reference set(s) automatically and then uses it for unsupervised extraction. We validate our approach with experimental results that show our unsupervised extraction is competitive with supervised machine learning approaches, including the previous supervised approach that exploits reference sets.

Journal

International Journal of Document Analysis and Recognition (IJDAR)Springer Journals

Published: Oct 16, 2007

There are no references for this article.