Semi-supervised Method for Extraction of Protein-Protein Interactions Using Hybrid Model

The poor performance and the lack of manual labeled corpus are two main problems in the task of protein-protein interaction extraction. A novel hybrid method is proposed. Based on the individual characteristics of machine learning and pattern learning, this method utilizes learned patterns from pattern learning to generate pattern features by performing sequence alignment. The pattern features and word features are incorporated into the input feature set of machine learning algorithms. The semi-supervised method based on k-nearest neighbours classifier is also proposed to train the hybrid method from unlabeled data automatically. Experimental results show the improved performance over the baseline methods with the hybrid model and the efficieny of the semi-supervised method for the lack of labeled data.