C4.5: programs for machine learning
C4.5: programs for machine learning
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Combining Weak Knowledge Sources for Sense Disambiguation
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Word sense disambiguation with pattern learning and automatic feature selection
Natural Language Engineering
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Assessing system agreement and instance difficulty in the lexical sample tasks of SENSEVAL-2
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Machine learning with lexical features: the Duluth approach to Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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We compare the word sense disambiguation systems submitted for the English-all-words task in SENSEVAL-2. We give several performance measures for the systems, and analyze correlations between system performance and word features. A decision tree learning algorithm is employed to discover the situations in which systems perform particularly well, and the resulting decision tree is examined. We investigate using a decision tree based on the SENSEVAL systems to (i) filter out senses unlikely to be correct, and to (ii) combine WSD systems. Some combinations created in this way outperform the best SENSEVAL system.