November 27, 1998
School of Information Technology and Engineering
University of Ottawa
Learning Relational Clichés with Contextual Generalization
Concept learners learn the definition of a concept from positive and negative examples of the concept. The definitions learned describe as many of the positives and as few of the negatives as possible. These definitions are then used to classify unknown examples as positive or negative examples. Many existing systems learn concepts one feature at a time. These systems have trouble learning definitions with interdependent features. The FOCL system (Pazzani et al. 1991) solved this problem by giving the concept learner hand-made "clichés" which are combinations of features. The problem is that these clichés are hard to derive. I developed CLUse (Clichés Learned and Used) to learn clichés automatically. Empirical testing shows that CLUse can help concept learners with useful clichés learned across domains.