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 (Quinlan, 1990) 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 other previous methods.

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In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, {S. Wermter, E. Riloff} and G. Scheler (Eds.), pp. 370-384, Berlin 1996. Springer.

@InCollection{mooney:bkchapter96, title={Learning the Past Tense of English Verbs Using Inductive Logic Programming}, author={Raymond J. Mooney and Mary Elaine Califf}, booktitle={Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing}, editor={{S. Wermter and E. Riloff} and G. Scheler}, address={Berlin}, publisher={Springer}, key={ilp nla}, pages={370-384}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:bkchapter96", year={1996} }