Login: A logic programming language with built-in inheritance
Journal of Logic Programming
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Machine Learning
The ups and downs of lexical acquisition
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning Logical Definitions from Relations
Machine Learning
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Induction of Logic Programs Based on psi-Terms
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Learning semantic-level information extraction rules by type-oriented ILP
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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This paper presents the type-oriented relational learner RHB+. Attaching type information to hypotheses is effective in avoiding overgeneralization as well as enhancing readability and comprehensibility. In many areas, such as NLP, type information is actually available, while negative examples are not. Unfortunately, learning performance is usually poor if types are attached when only positive examples are available. RHB+ makes use of type information to efficiently compute informativity from positive examples only and to judge a stopping condition. The new technique of dynamic type restriction by positive examples lets covered positive examples decide the types appropriate for the current clause. The current version of RHB+, written in the typed logic programming language LIFE, directly manipulates types as structured background knowledge when operations related to types are required. These features make RHB+ efficient and effective in attaching types selected from thousands of possible types. This leads to advantages over several previous learners, such as FOIL and PROGOL. Experimental results demonstrate RHB+ 's fine performance for both artificial and real data.