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Semi-supervised learning of the hidden vector state model for extracting protein-protein interactions

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Semi-supervised learning of the hidden vector state model for extracting protein-protein interactions. Artificial Intelligence in Medicine, 41(3) pp. 209–222.

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The hidden vector state (HVS) model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. It has been applied successfully for protein–protein interactions extraction. However, the HVS model, being a statistically based approach, requires large-scale annotated corpora in order to reliably estimate model parameters. This is normally difficult to obtain in practical applications.

Methods and materials

In this paper, we present two novel semi-supervised learning approaches, one based on classification and the other based on expectation-maximization, to train the HVS model from both annotated and un-annotated corpora.

Results and conclusion

Experimental results show the improved performance over the baseline system using the HVS model trained solely from the annotated corpus, which gives the support to the feasibility and efficiency of our approaches.

Item Type: Journal Article
Copyright Holders: 2007 Elsevier B.V.
ISSN: 1873-2860
Keywords: semi-supervised learning; hidden vector state model; protein–protein interactions; information extraction
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23797
Depositing User: Kay Dave
Date Deposited: 03 Mar 2011 10:29
Last Modified: 27 Mar 2016 19:42
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