Zhou, Deyu; He, Yulan and Kwoh, Chee Keong
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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1016/j.artmed.2007.07.004|
|Google Scholar:||Look up in Google Scholar|
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.|
|Keywords:||semi-supervised learning; hidden vector state model; protein–protein interactions; information extraction|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||03 Mar 2011 10:29|
|Last Modified:||09 Oct 2016 07:57|
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