A STACKED GRAPHICAL MODEL FOR ASSOCIATING INFORMATION FROM TEXT AND IMAGES IN FIGURES
Zhenzhen Kou, William W. Cohen, Robert F. Murphy · Europe PMC (PubMed Central) · 2007
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 introduced a stacked graphical model to match the labels of sub-figures with labels of sentences. The experimental results show that the stacked graphical model can take advantage of the context information and achieve a satisfactory accuracy. 1.