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 shallow parser based on closed-class words to capture relations in biomedical text
Journal of Biomedical Informatics
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
Artificial Intelligence in Medicine
RelEx---Relation extraction using dependency parse trees
Bioinformatics
IntEx: a syntactic role driven protein-protein interaction extractor for bio-medical text
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
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
Simplicity is better: revisiting single kernel PPI extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A hybrid approach to extract protein–protein interactions
Bioinformatics
Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
DTMBIO 2012: international workshop on data and text mining in biomedical informatics
Proceedings of the 21st ACM international conference on Information and knowledge management
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Virtually all current PPI extraction studies focus on improving F-score, aiming to balance the performance on both precision and recall. However, in many realistic scenarios involving large corpora, one can benefit more from an extremely high precision PPI extraction tool than a high-recall counterpart. We also argue that the current "per-instance" basis performance evaluation method should be revisited. In order to address these problems, we introduce a new rule-based PPI extraction method equipped with a set of ultra-high precision extraction rules. We also propose a new "per-pair" basis performance metric, which is more pragmatic in practice. The proposed PPI extraction method achieves 95-96% per-pair and 94-97% per-instance precisions on the AIMed benchmark corpus.