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
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th 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
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
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
Extracting relation information from text documents by exploring various types of knowledge
Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
Tree kernels for semantic role labeling
Computational Linguistics
Computer Speech and Language
A novel feature-based approach to Chinese entity relation extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Discriminative reordering with Chinese grammatical relations features
SSST '09 Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Study of kernel-based methods for Chinese relation extraction
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel
IEEE Transactions on Audio, Speech, and Language Processing
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
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 article proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction between named entities. The basic idea is to employ constituent dependency information in keeping the necessary nodes and their head children along the path connecting the two entities in the syntactic parse tree, while removing the noisy information from the tree, eventually leading to a dynamic syntactic parse tree. This article also explores various entity features and their possible combinations via a unified syntactic and semantic tree framework, which integrates both structural syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 English and 2005 Chinese benchmark corpora shows that our dynamic syntactic parse tree much outperforms all previous tree spans, indicating its effectiveness in well representing the structural nature of relation instances while removing redundant information. Moreover, the unified parse and semantic tree significantly outperforms the single syntactic parse tree, largely due to the remarkable contributions from entity-related semantic features such as its type, subtype, mention-level as well as their bi-gram combinations. Finally, the best performance so far in semantic relation extraction is achieved via a composite kernel, which combines this tree kernel with a linear, state-of-the-art, feature-based kernel.