Figure Classification in Biomedical Literature towards Figure Mining

Abstract

Biomedical papers contain large amounts of figures. Since they provide important information about research outcomes, mining techniques targeting them have attracted a great deal of attention. Our final goal is to develop a figure finding system, FigFinder, to retrieve figures relevant to a userpsilas query by mining information contained in figures, their legends, and the main text in an integrative manner. In this study, we worked on figure classification to choose those representing signaling or metabolic pathways, based on textual information contained in biomedical papers, as the first step to develop FigFinder. We took several supervised machine learning methods, and could confirm that the use of main text combined with figure legends was quite effective. Although many groups have considered figure legends, this is the first attempt to address figure classification task by utilizing figure legends together with main text to our knowledge.