Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A corpus-based approach to language learning
A corpus-based approach to language learning
Unifying instance-based and rule-based induction
Machine Learning
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Machine Learning
Instance-Family Abstraction in Memory-Based Language Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Memory-Based Learning of morphology with stochastic transducers
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Rule stacking: an approach for compressing an ensemble of rule sets into a single classifier
DS'11 Proceedings of the 14th international conference on Discovery science
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An extension to memory-based learning is described in which automatically induced rules are used as binary features. These features have an "active" value when the left-hand side of the underlying rule applies to the instance. The RIPPER rule induction algorithm is adopted for the selection of the underlying rules. The similarity of a memory instance to a new instance is measured by taking the sum of the weights of the matching rules both instances share. We report on experiments that indicate that (i) the method works equally well or better than RIPPER on various language learning and other benchmark datasets; (ii) the method does not necessarily perform better than default memory-based learning, but (iii) when multi-valued features are combined with the rule-based features, some slight to significant improvements are observed.