Extracting Protein-Protein Interaction Information from Biomedical Text with SVM
IEICE - Transactions on Information and Systems
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
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
A rich feature vector for protein-protein interaction extraction from multiple corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Tree kernel-based protein-protein interaction extraction from biomedical literature
Journal of Biomedical Informatics
Improving distantly supervised extraction of drug-drug and protein-protein interactions
ROBUS-UNSUP '12 Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
High precision rule based PPI extraction and per-pair basis performance evaluation
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
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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Recent kernel-based PPI extraction systems achieve promising performance because of their capability to capture structural syntactic information, but at the expense of computational complexity. This paper incorporates dependency information as well as other lexical and syntactic knowledge in a feature-based framework. Our motivation is that, considering the large amount of biomedical literature being archived daily, feature-based methods with comparable performance are more suitable for practical applications. Additionally, we explore the difference of lexical characteristics between biomedical and newswire domains. Experimental evaluation on the AIMed corpus shows that our system achieves comparable performance of 54.7 in F1-Score with other state-of-the-art PPI extraction systems, yet the best performance among all the feature-based ones.