Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Artificial Intelligence in Medicine
Methodological Review: Extracting interactions between proteins from the literature
Journal of Biomedical Informatics
Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model
International Journal of Bioinformatics Research and Applications
Training the Hidden Vector State Model from Un-annotated Corpus
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Multiple kernel learning in protein-protein interaction extraction from biomedical literature
Artificial Intelligence in Medicine
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In the field of bioinformatics in solving biological problems, the huge amount of knowledge is often locked in textual documents such as scientific publications. Hence there is an increasing focus on extracting information from this vast amount of scientific literature. In this paper, we present an information extraction system which employs a semantic parser using the Hidden Vector State (HVS) model for protein-protein interactions. Unlike other hierarchical parsing models which require fully annotated treebank data for training, the HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure needed to robustly extract task domain semantics. When applied in extracting protein-protein interactions information from medical literature, we found that it performed better than other established statistical methods and achieved 47.9% and 72.8% in recall and precision respectively.