Structured correspondence topic models for mining captioned figures in biological literature

A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents. In biological articles, a typical figure often comprises multiple panels, accompanied by either scoped or global captioned text. Moreover, the text in the caption contains important semantic entities such as protein names, gene ontology, tissues labels, etc., relevant to the images in the figure. Due to the avalanche of biological literature in recent years, and increasing popularity of various bio-imaging techniques, automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science. We present a new structured probabilistic topic model built on a realistic figure generat...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Amr Ahmed, Eric P. Xing, William W. Cohen, Robert F. Murphy
• Keywords: Biological Figures, Data Mining, Figure Generation Scheme, KDD 2009, Major Unsolved Challenge