Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A shallow parser based on closed-class words to capture relations in biomedical text
Journal of Biomedical Informatics
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Information Processing and Management: an International Journal
Methodological Review: Extracting interactions between proteins from the literature
Journal of Biomedical Informatics
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
Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Pathway construction and extension using natural language processing
HCI International'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction for health, safety, mobility and complex environments - Volume Part II
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It has been known that a combination of multiple kernels and addition of various resources are the best options for improving effectiveness of kernel-based PPI extraction methods. These supplements, however, involve extensive kernel adaptation and feature selection processes, which attenuate the original benefits of the kernel methods. This paper shows that we are able to achieve the best performance among the state-of-the-art methods by using only a single kernel, convolution parse tree kernel. In-depth analyses of the kernel reveal that the keys to the improvement are the tree pruning method and consideration of tree kernel decay factors. It is noteworthy that we obtained the performance without having to use any additional features, kernels or corpora.