document: 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 generation scheme to model the structurally annotated biological figures , and we derive an efficient inference algorithm based on collapsed Gibbs sampling for information retrieval and visualization . The resulting program constitutes one of the key IR engines in our SLIF system that has recently entered the final round ( 4 out 70 competing systems ) of the Elsevier Grand Challenge on Knowledge Enhancement in the Life Science . Here we present various evaluations on a number of data mining tasks to illustrate our method .
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