Title
Unsupervised information extraction from unstructured, ungrammatical data sources on the World Wide Web
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.
Year
DOI
Venue
2007
10.1007/s10032-007-0052-2
International Journal on Document Analysis and Recognition
Keywords
DocType
Volume
world wide web,information integration,information extraction
Journal
10
Issue
ISSN
Citations 
3
1433-2833
15
PageRank 
References 
Authors
0.75
18
2
Name
Order
Citations
PageRank
Matthew Michelson140922.23
Craig A. Knoblock25229680.57