The nature of statistical learning theory
The nature of statistical learning theory
Journal of Intelligent Information Systems
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Word sense disambiguation in information retrieval revisited
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
Latent linkage semantic kernels for collective classification of link data
Journal of Intelligent Information Systems
A kernel PCA method for superior word sense disambiguation
ACL '04 Proceedings of the 42nd 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
Acquiring Word Similarities with Higher Order Association Mining
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Kernel methods for word sense disambiguation and acronym expansion
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Kernel methods for minimally supervised wsd
Computational Linguistics
A framework for understanding Latent Semantic Indexing (LSI) performance
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Higher Order Naïve Bayes: A Novel Non-IID Approach to Text Classification
IEEE Transactions on Knowledge and Data Engineering
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Sprinkling: supervised latent semantic indexing
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Word sense disambiguation improves information retrieval
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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The success of machine learning approaches to word sense disambiguation (WSD) is largely dependent on the representation of the context in which an ambiguous word occurs. Typically, the contexts are represented as the vector space using ''Bag of Words (BoW)'' technique. Despite its ease of use, BoW representation suffers from well-known limitations, mostly due to its inability to exploit semantic similarity between terms. In this paper, we apply the semantic diffusion kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information, to smooth the BoW representation for WSD systems. Semantic diffusion kernel can be obtained through a matrix exponentiation transformation on the given kernel matrix, and virtually exploits higher order co-occurrences to infer semantic similarity between terms. The superiority of the proposed method is demonstrated experimentally with several SensEval disambiguation tasks.