Zhou, Deyu; He, Yulan and Kwoh, Chee Keong
(2006).
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| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1007/11758525_97 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
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 |
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| 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: | 25 Oct 2012 23:19 |
| URI: | http://oro.open.ac.uk/id/eprint/23801 |
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