title: A stacked graphical model for associating sub-images with sub-captions.
abstract: There is extensive interest in mining data from full text. We have built a system called SLIF (for Subcellular Location Image Finder), which extracts information on one particular aspect of biology from a combination of text and images in journal articles. Associating the information from the text and image requires matching sub-figures with the sentences in the text. We introduce a stacked graphical model, a meta-learning scheme to augment a base learner by expanding features based on related instances, to match the labels of sub-figures with labels of sentences. The experimental results show a significant improvement in the matching accuracy of the stacked graphical model (81.3%) as compared with a relational dependency network (70.8%) or the current algorithm in SLIF (64.3%).
authors.affiliation: Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu
authors.forename: Zhenzhen
authors.initials: Z
authors.lastname: Kou
authors.forename: William W
authors.initials: WW
authors.lastname: Cohen
authors.forename: Robert F
authors.initials: RF
authors.lastname: Murphy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003196:Computer Graphics; D003198:Computer Simulation; D016208:Databases, Factual; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted; D013347:Subcellular Fractions / Q000648:ultrastructure*