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Relational Learning with Statistical Predicate Invention: Better Models for
Hypertext
Mark Craven
and Sean Slattery.
Machine Learning, 43(1/2):97--119, April 2001.
Abstract
We present a new approach to learning hypertext classifiers that combines a
statistical text-learning method with a relational rule learner. This
approach is well suited to learning in hypertext domains because its
statistical component allows it to characterize text in terms of word
frequencies. whereas its relational component is able to describe how
neighboring documents are related to each other by hyperlinks that connect
them. We evaluate our approach by applying it to tasks that involve learning
definitions for (i) classes of pages, (ii) particular relations that exist
between pairs of pages, and (iii) locating a particular class of information
in the internal structure of pages. Our experiments demonstrate that this new
approach is able to learn more accurate classifiers than either of its
constituent methods alone.
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