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Relational Learning of Pattern-Match Rules for Information Extraction
M. E. Califf
and R. J. Mooney.
In Working Notes of AAAI Spring Symposium on Applying Machine Learning to
Discourse Processing, pages 6--11, Menlo Park, CA, 1998. More behind this link.. AAAI Press
Abstract
Information extraction is a form of shallow text processing which locates a
specified set of relevant items in natural language documents. Such systems
can be useful, but require domain-specific knowledge and rules, and are
time-consuming and difficult to build by hand, making infomation extraction a
good testbed for the application of machine learning techniques to natural
language processing. This paper presents a system, RAPIER, that takes pairs
of documents and filled templates and induces pattern-match rules that
directly extract fillers for the slots in the template. The learning
algorithm incorporates techniques from several inductive logic programming
systems and learns unbounded patterns that include constraints on the words
and part-of-speech tags surrounding the filler. Encouraging results are
presented on learning to extract information from computer job postings from
the newsgroup misc.jobs.offered.
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