Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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
Kernel methods for relation extraction
The Journal of Machine Learning Research
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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
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
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Composite kernels for relation extraction
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Semantic relation extraction with kernels over typed dependency trees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Simplicity is better: revisiting single kernel PPI extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
ACM Transactions on Asian Language Information Processing (TALIP)
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
Incorporating lexical semantic similarity to tree kernel-based chinese relation extraction
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Learning bayesian network using parse trees for extraction of protein-protein interaction
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction.