Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model
http://data.open.ac.uk/oro/23801
is a Article , Academic article

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Dataset Open Research Online
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At ICCS 2006: 6th international conference
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Date 2006
Status Peer reviewed
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  • http://data.open.ac.uk/oro/document/11184
  • http://data.open.ac.uk/oro/document/14768
  • http://data.open.ac.uk/oro/document/25900
  • http://data.open.ac.uk/oro/document/6224
Volume 3992
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.
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  • 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.
  • 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.
Same as 11758525_97
Title Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model