Kernel methods for relation extraction
The Journal of Machine Learning Research
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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
Entity-focused sentence simplification for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Automated extraction of protein-protein interactions (PPIs) from biomedical literatures is an important topic of biomedical text mining. In this paper, we propose an approach based on neighborhood hash graph kernel for this task. In contrast to the existing graph kernel-based approaches for PPI extraction, the proposed approach not only has the capability to make use of full dependency graphs to represent the sentence structure but also effectively control the computational complexity. We evaluate the proposed approach on five publicly available PPI corpora and perform detailed comparisons with other approaches. The experimental result shows that our approach is comparable to the state-of-the-art PPI extraction system and much faster than all-path graph kernel approach on all five PPI corpora.