DocumentCode :
619840
Title :
Expert evidence document recognition integrating relationship of expert
Author :
Meng Wang ; Tao Shen ; Zhengtao Yu
Author_Institution :
Sch. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China
fYear :
2013
fDate :
25-27 May 2013
Firstpage :
997
Lastpage :
1002
Abstract :
Focus on the issue that the existing expert evidence document recognition method is not effective use of the relationship between experts, this paper proposed a method to recognize the expert evidence documents which integrate the feature of webpage content and the complex relationship between the experts. First of all, the method analyses characteristics of expert evidence document, to select Coauthors and organization as relationship feature, and then extracts feature values. Secondly, select 10130 expert evidence pages, and build data sets which integrate the feature values of and webpage content and the relationship, and then select different learning algorithms to build recognition model. Finally, experiment of expert evidence document recognition is carried out in the field of information processing. The experimental results shows that the proposed method achieves a good recognition effect. Using relationship can effectively improve the accuracy of the evidence document recognition. Its accuracy is improved by 4% compared to method using the page content feature recognition only.
Keywords :
document image processing; feature extraction; image reconstruction; learning (artificial intelligence); Web page content; coauthors feature; expert evidence document recognition method; expert relationship; feature value extraction; information processing; learning algorithm; organization feature; page content feature recognition; Accuracy; Classification algorithms; Data mining; Encyclopedias; Feature extraction; Organizations; Training data; Adaboost algorithm; Content feature; Expert evidence document recognition; Relationship feature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
Type :
conf
DOI :
10.1109/CCDC.2013.6561069
Filename :
6561069
Link To Document :
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