Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
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
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
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
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Improving Relation Extraction by Exploiting Properties of the Target Relation
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Hi-index | 0.00 |
This paper proposes a ternary relation extraction method primarily based on rich syntactic information. We identify PROTEIN-ORGANISM-LOCATION relations in the text of biomedical articles. Different kernel functions are used with an SVM learner to integrate two sources of information from syntactic parse trees: (i) a large number of syntactic features that have been shown useful for Semantic Role Labeling (SRL) and applied here to the relation extraction task, and (ii) features from the entire parse tree using a tree kernel. Our experiments show that the use of rich syntactic features significantly outperforms shallow word-based features. The best accuracy is obtained by combining SRL features with tree kernels.