Chen, Ying


Label Ranking by Directly Optimizing Performance Measures

AAAI Conferences

Label ranking aims to map instances to an order over a predefined set of labels. It is ideal that the label ranking model is trained by directly maximizing performance measures on training data. However, existing studies on label ranking models mainly based on the minimization of classification errors or rank losses. To fill in this gap in label ranking, in this paper a novel label ranking model is learned by minimizing a loss function directly defined on the performance measures. The proposed algorithm, referred to as BoostLR, employs a boosting framework and utilizes the rank aggregation technique to construct weak label rankers. Experimental results reveal the initial success of BoostLR.


Cross Media Entity Extraction and Linkage for Chemical Documents

AAAI Conferences

Text and images are two major sources of information in scientific literature. Information from these two media typically reinforce and complement each other, thus simplifying the process for human to extract and comprehend information. However, machines cannot create the links or have the semantic understanding between images and text. We propose to integrate text analysis and image processing techniques to bridge the gap between the two media, and discover knowledge from the combined information sources, which would be otherwise lost by traditional single-media based mining systems. The focus is on the chemical entity extraction task because images are well known to add value to the textual content in chemical literature. Annotation of US chemical patent documents demonstrates the effectiveness of our proposal.