Improving PGF retrieval effectiveness with active learning

Multimedia education is playing a significant and increasing role for education purposes, thus leading to a large number of electronic documents. Plane geometry figures (PGFs), as important components of these documents, are regarded as very helpful information to most retrieval systems in the field of mathematics education. However, the burdensome work of annotation has become one of the chief obstacles to improve the efficiency of retrieval systems. In this paper, we introduce an active learning-based frame to select candidate instances for training the classifiers in retrieval systems, which are an emerging non-text-based information systems. In addition, an enhanced uncertainty measure and the selection of specific features of PGFs are proposed for our active learning algorithm. Comparative experiment results indicate that the proposed method effectively improves the performance of the PGF retrieval system and reduces the burdensome annotation workload.