Integrating Active Learning Strategy to the Ensemble Kernel-based Method for Protein-Protein Interaction Extraction

This paper presents an ensemble kernelbased active learning method for PPI (Protein-protein interaction) extraction. This ensemble kernel is composed of feature-based kernel and structure-based kernel. Experimental results show that the F-scores of PPI extraction using ensemble kernel model on AIMED (Abstracts in medline), IEPA (the Interaction extraction performance assessment corpus) and BCPPI (Biocreative PPI dataset) corpora are 64.50%, 69.74% and 60.38% respectively. As the passive learning methods need large labeled data sets and it is expensive to label data manually, we integrate active learning strategy into the ensemble kernel model. The uncertainty-based sampling strategy is used in the active learning method. Two experiments for active learning are conducted on AIMED, IEPA, BCPPI corpus. The experimental results integrating the active learning strategy show that the F-scores on AIMED, IEPA and BCPPI corpora are better than those using the passive learning, and meantime reduce the labeling data.