Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Kernel methods for relation extraction
The Journal of Machine Learning Research
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
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
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
Multi-task transfer learning for weakly-supervised relation extraction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semi-supervised learning for semantic relation classification using stratified sampling strategy
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A study of convolution tree kernel with local alignment
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Joint entity and relation extraction using card-pyramid parsing
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Clustering-based stratified seed sampling for semi-supervised relation classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A tree kernel-based unified framework for Chinese zero anaphora resolution
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Dependency-driven anaphoricity determination for coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Semi-supervised relation extraction with large-scale word clustering
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A combination of topic models with max-margin learning for relation detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Tree kernel-based protein-protein interaction extraction from biomedical literature
Journal of Biomedical Informatics
A case for semantic full-text search
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
Compensating for annotation errors in training a relation extractor
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Incorporating lexical semantic similarity to tree kernel-based chinese relation extraction
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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This paper proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction. It exploits constituent dependencies to keep the nodes and their head children along the path connecting the two entities, while removing the noisy information from the syntactic parse tree, eventually leading to a dynamic syntactic parse tree. This paper also explores entity features and their combined features in a unified parse and semantic tree, which integrates both structured syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 corpus shows that our dynamic syntactic parse tree outperforms all previous tree spans, and the composite kernel combining this tree kernel with a linear state-of-the-art feature-based kernel, achieves the so far best performance.