An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text chunking by combining hand-crafted rules and memory-based learning
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Boosting trees for clause splitting
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Self-organizing η-gram model for automatic word spacing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Introduction to the CoNLL-2001 shared task: clause identification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Clause boundary recognition using support vector machines
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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Identification of dependency relation among clauses is one of the most critical parts in parsing Korean sentences because it generates severe ambiguities. The resolution of the ambiguities involves both syntactic and semantic information. This paper proposes a method to determine the dependency relation among Korean clauses using parse tree kernels. The parse tree used in this paper provides the method with the syntactic information, and the endings (Eomi) do with the semantic information. In addition, the parse tree kernel for handling parse trees has benefits that it minimizes the information loss occurred during transforming a parse tree into a feature vector, and can obtain, as a result, very accurate similarity between parse trees. The experimental results on a standard Korean data set show 89.12% of accuracy, which implies that the proposed method is plausible for the dependency analysis of clauses.