An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Efficient Computation of Gapped Substring Kernels on Large Alphabets
The Journal of Machine Learning Research
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using string-kernels for learning semantic parsers
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A hybrid convolution tree kernel for semantic role labeling
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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
Learning to transform natural to formal languages
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Joint entity and relation extraction using card-pyramid parsing
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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This paper introduces a new kernel which computes similarity between two natural language sentences as the number of paths shared by their dependency trees. The paper gives a very efficient algorithm to compute it. This kernel is also an improvement over the word subsequence kernel because it only counts linguistically meaningful word subsequences which are based on word dependencies. It overcomes some of the difficulties encountered by syntactic tree kernels as well. Experimental results demonstrate the advantage of this kernel over word subsequence and syntactic tree kernels.