Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Combining contextual features for word sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A kernel PCA method for superior word sense disambiguation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
English lexical sample task description
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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
Semi-supervised learning integrated with classifier combination for word sense disambiguation
Computer Speech and Language
Semi-supervised Word Sense Disambiguation Using the Web as Corpus
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Investigating problems of semi-supervised learning for word sense disambiguation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Word sense disambiguation by semi-supervised learning
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Supervised word sense disambiguation using semantic diffusion kernel
Engineering Applications of Artificial Intelligence
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In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Principal Component Analysis (KPCA), with experiments showing that it can further improve accuracy over supervised KPCA models that have achieved WSD accuracy superior to the best published individual models. Although empirical results with supervised KPCA models demonstrate significantly better accuracy compared to the state-of-the-art achieved by either naïve Bayes or maximum entropy models on Senseval-2 data, we identify specific sparse data conditions under which supervised KPCA models deteriorate to essentially a most-frequent-sense predictor. We discuss the potential of KPCA for leveraging unannotated data for partially-unsupervised training to address these issues, leading to a composite model that combines both the supervised and semi-supervised models.