Algorithms for clustering data
Algorithms for clustering data
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
On a Kernel Regression Approach to Machine Translation
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Kernel regression based machine translation
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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In this paper, we present a novel clustering approach based on the use of kernels as similarity functions and the C-means algorithm. Several word-sequence kernels are defined and extended to verify the properties of similarity functions. Afterwards, these monolingual wordsequence kernels are extended to bilingual word-sequence kernels, and applied to the task of monolingual and bilingual sentence clustering. The motivation of this proposal is to group similar sentences into clusters so that specialised models can be trained for each cluster, with the purpose of reducing in this way both the size and complexity of the initial task.We provide empirical evidence for proving that the use of bilingual kernels can lead to better clusters, in terms of intra-cluster perplexities.