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» Probabilistic Declarative Information Extraction
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PAKDD
2010
ACM
167views Data Mining» more  PAKDD 2010»
13 years 11 months ago
Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand
We present a general framework for the task of extracting specific information “on demand” from a large corpus such as the Web under resource-constraints. Given a database wit...
Pallika Kanani, Andrew McCallum, Shaohan Hu
CEAS
2004
Springer
14 years 1 months ago
Extracting social networks and contact information from email and the Web
We present an end-to-end system that extracts a user’s social network and its members’ contact information given the user’s email inbox. The system identifies unique people...
Aron Culotta, Ron Bekkerman, Andrew McCallum
DAS
2010
Springer
13 years 5 months ago
Information extraction by finding repeated structure
Repetition of layout structure is prevalent in document images. In document design, such repetition conveys the underlying logical and functional structure of the data. For exampl...
Evgeniy Bart, Prateek Sarkar
ICML
2000
IEEE
14 years 8 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
NGITS
1995
Springer
13 years 11 months ago
Category Translation: Learning to Understand Information on the Internet
This paper investigates the problem ofautomatically learning declarative models of information sources available on the Internet. We report on ILA, a domain-independent program th...
Mike Perkowitz, Oren Etzioni