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Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2006). Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model. In: Computational Science � ICCS 2006, pp. 718–725.

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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.

Item Type: Conference Item
Copyright Holders: 2006 Springer-Verlag
Extra Information: Lecture Notes in Computer Science
Part 2 of proceedings
ISBN: 978-3-540-34381-3
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23801
Depositing User: Kay Dave
Date Deposited: 30 Mar 2011 09:58
Last Modified: 22 Mar 2016 19:56
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