Making large-scale support vector machine learning practical
Advances in kernel methods
Accurate unlexicalized parsing
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
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
RelEx---Relation extraction using dependency parse trees
Bioinformatics
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
A hybrid approach to extract protein–protein interactions
Bioinformatics
A study on dependency tree kernels for automatic extraction of protein-protein interaction
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Any domain parsing: automatic domain adaptation for natural language parsing
Any domain parsing: automatic domain adaptation for natural language parsing
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Using a shallow linguistic kernel for drug-drug interaction extraction
Journal of Biomedical Informatics
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Kernel based methods dominate the current trend for various relation extraction tasks including protein-protein interaction (PPI) extraction. PPI information is critical in understanding biological processes. Despite considerable efforts, previously reported PPI extraction results show that none of the approaches already known in the literature is consistently better than other approaches when evaluated on different benchmark PPI corpora. In this paper, we propose a novel hybrid kernel that combines (automatically collected) dependency patterns, trigger words, negative cues, walk features and regular expression patterns along with tree kernel and shallow linguistic kernel. The proposed kernel outperforms the exiting state-of-the-art approaches on the BioInfer corpus, the largest PPI benchmark corpus available. On the other four smaller benchmark corpora, it performs either better or almost as good as the existing approaches. Moreover, empirical results show that the proposed hybrid kernel attains considerably higher precision than the existing approaches, which indicates its capability of learning more accurate models. This also demonstrates that the different types of information that we use are able to complement each other for relation extraction.