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by Raymond J. Mooney, Mary Elaine Califf
Symbolic, Connectionist and Statistical Approaches to Learning for Natural Language Pr ocessing, Springer-Verlag
ftp://ftp.cs.utexas.edu/pub/mooney/papers/foidl-bkchapter-95.ps.Z
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Abstract:
Abstract. This paper presents results on using a new inductive logic programming method called Foidl to learn the past tense of English verbs. The past tense task has been widely studied in the context of the symbolic/connectionist debate. Previous papers have presented results using various neural-network and decision-tree learning methods. We have developed a technique for learning a special type of Prolog program called a first-order decision list, defined as an ordered list of clauses each ending in a cut. Foidl is based on Foil [19] but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as the past-tense task. We present results showing that Foidl learns a more accurate past-tense generator from significantly fewer examples than all previous methods. 1
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