Foundations of statistical natural language processing
Foundations of statistical natural language processing
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Combining Classifiers for word sense disambiguation
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
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Combining heterogeneous classifiers for word-sense disambiguation
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
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data & Knowledge Engineering
Combining classifiers based on OWA operators with an application to word sense disambiguation
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Combining classifiers with multi-representation of context in word sense disambiguation
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Classifier combination is a promising way to improve performance of word sense disambiguation. We propose a new combinational method in this paper. We first construct a series of Naïve Bayesian classifiers along a sequence of orderly varying sized windows of context, and perform sense selection for both training samples and test samples using these classifiers. We thus get a sense selection trajectory along the sequence of context windows for each sample. Then we make use of these trajectories to make final k-nearest-neighbors-based sense selection for test samples. This method aims to lower the uncertainty brought by classifiers using different context windows and make more robust utilization of context while perform well. Experiments show that our approach outperforms some other algorithms on both robustness and performance.