NDLI: A probabilistic approach for adapting information extraction wrappers and discovering new attributes

Content Provider	IEEE Xplore Digital Library
Author	Tak-Lam Wong Wai Lam
Copyright Year	2004
Description	Author affiliation: Dept. of Syst. Eng. & Eng. Manage., Hong Kong Chinese Univ., China (Tak-Lam Wong; Wai Lam)
Abstract	We develop a probabilistic framework for adapting information extraction wrappers with new attribute discovery. Wrapper adaptation aims at automatically adapting a previously learned wrapper from the source Web site to a new unseen site for information extraction. One unique characteristic of our framework is that it can discover new or previously unseen attributes as well as headers from the new site. It is based on a generative model for the generation of text fragments related to attribute items and formatting data in a Web page. To solve the wrapper adaptation problem, we consider two kinds of information from the source Web site. The first kind of information is the extraction knowledge contained in the previously learned wrapper from the source Web site. The second kind of information is the previously extracted or collected items. We employ a Bayesian learning approach to automatically select a set of training examples for adapting a wrapper for the new unseen site. To solve the new attribute discovery problem, we develop a model which analyzes the surrounding text fragments of the attributes in the new unseen site. A Bayesian learning method is developed to discover the new attributes and their headers. EM technique is employed in both Bayesian learning models. We conducted extensive experiments from a number of real-world Web sites to demonstrate the effectiveness of our framework.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	257
Ending Page	264
File Size	383026
Page Count	8
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Web pages Bayesian methods Learning systems Books Humans Systems engineering and theory Research and development management Web sites Web search
Content Type	Text
Resource Type	Article

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