Journal of Intelligent Information Systems
Kernel methods for relation extraction
The Journal of Machine Learning Research
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Protein homology detection using string alignment kernels
Bioinformatics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A generalization of Haussler's convolution kernel: mapping kernel
Proceedings of the 25th international conference on Machine learning
Tree kernels for semantic role labeling
Computational Linguistics
Semantic classification with distributional kernels
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Exploiting constituent dependencies for tree kernel-based semantic relation extraction
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Word sense disambiguation for exploiting hierarchical thesauri in text classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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This paper discusses a new convolution tree kernel by introducing local alignments. The main idea of the new kernel is to allow some syntactic alternations during each match between subtrees. In this paper, we give an algorithm to calculate the composite kernel. The experiment results show promising improvements on two tasks: semantic role labeling and question classification.