The nature of statistical learning theory
The nature of statistical learning theory
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Named entity recognition using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
English lexical sample task description
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Supervised sense tagging using support vector machines
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
The Johns Hopkins SENSEVAL2 system descriptions
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Word sense disambiguation vs. statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semi-supervised training of a kernel PCA-based model for word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Applying alternating structure optimization to word sense disambiguation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Chinese abbreviation-definition identification: a SVM approach using context information
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An experimental study on unsupervised graph-based word sense disambiguation
CICLing'10 Proceedings of the 11th 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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We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either naïve Bayes or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. It is hoped that these highly encouraging first results on KPCA for natural language processing tasks will inspire further development of these directions.