A STACKED GRAPHICAL MODEL FOR ASSOCIATING INFORMATION FROM TEXT AND IMAGES IN FIGURES
Identifieur interne : 000144 ( Pmc/Corpus ); précédent : 000143; suivant : 000145A STACKED GRAPHICAL MODEL FOR ASSOCIATING INFORMATION FROM TEXT AND IMAGES IN FIGURES
Auteurs : Zhenzhen Kou ; William W. Cohen ; Robert F. MurphySource :
- Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing ; 2007.
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 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.
Url:
PubMed: 17990497
PubMed Central: 2853925
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