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


CONCLUSIONS
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.
Zhou
Deyu
D
School of Computer Engineering, Nanyang Technological University, Block N4, Nanyang Avenue, Singapore 639798, Singapore. zhou0063@ntu.edu.sg
He
Yulan
Y
Kwoh
Chee Keong
CK
D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030562:Databases, Protein; D005240:Feasibility Studies; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D015203:Reproducibility of Results
D011506:Proteins