Multi-Task Linear Dependency Modeling for drug-related webpages classification

In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental results show that Multi-Task Linear Dependency Modeling outperforms existing decision level and feature level combination methods and achieves the best performance.