Machine Learning
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
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
Hierarchical directed acyclic graph kernel: methods for structured natural language data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Speeding up training with tree kernels for node relation labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A generalization of Haussler's convolution kernel: mapping kernel
Proceedings of the 25th international conference on Machine learning
Locality kernels for sequential data and their applications to parse ranking
Applied Intelligence
Syntactic kernels for natural language learning: the semantic role labeling case
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
The graph neural network model
IEEE Transactions on Neural Networks
The Journal of Machine Learning Research
Two-tier similarity model for story link detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Locality-convolution kernel and its application to dependency parse ranking
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing (NLP) tasks. Experiments have, however, shown that the over-fitting problem often arises when these kernels are used in NLP tasks. This paper discusses this issue of convolution kernels, and then proposes a new approach based on statistical feature selection that avoids this issue. To enable the proposed method to be executed efficiently, it is embedded into an original kernel calculation process by using sub-structure mining algorithms. Experiments are undertaken on real NLP tasks to confirm the problem with a conventional method and to compare its performance with that of the proposed method.