Complexity of lexical descriptions and its relevance to partial parsing
Complexity of lexical descriptions and its relevance to partial parsing
An Investigation of Transformation-Based Learning in Discourse
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Lazy Transformation-Based Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Dialogue act tagging with Transformation-Based Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Error driven word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Efficient transformation-based parsing
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
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Transformation-Based Learning (TBL) is a relatively new machine learning method that has achieved notable success on language problems. This paper presents a variant of TBL, called Randomized TBL, that overcomes the training time problems of standard TBL without sacrificing accuracy. It includes a set of experiments on part-of-speech tagging in which the size of the corpus and template set are varied. The results show that Randomized TBL can address problems that are intractable in terms of training time for standard TBL. In addition, for language problems such as dialogue act tagging where the most effective features have not been identified through linguistic studies, Randomized TBL allows the researcher to experiment with a large set of templates capturing many potentially useful features and feature interactions.