JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (3): 111-115.doi: 10.6040/j.issn.1671-9352.1.2015.C07

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Semi-supervised method for attribute extraction based on transductive learning

SU Feng-long1, XIE Qing-hua2*, HUANG Qing-quan1, QIU Ji-yuan1, YUE Zhen-jun1   

  1. 1. Institute of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, Jiangsu, China;
    2. Institute of National Defense Engineering, PLA University of Science and Technology, Nanjing 210007, Jiangsu, China
  • Received:2015-10-30 Online:2016-03-20 Published:2016-04-07

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Abstract: In the study of text information extraction, aiming at the default of traditional supervised learning method that manual text annotation is high time-consuming and large workload, an improved semi-supervised learning model was proposed. The model combined support vector machines advantage of classification with transductive learnings advantage of generalization, which used a small amount of tagged corpus to study and test and then added them to the training model to adjust the prediction gradually. In attributes extraction experiments, this model performed better compared with traditional support vector machine methods. The generalization ability of the model was strong, which saved a great deal of manual cost.

Key words: transductive support vector machine, semi-supervised learning, information extraction, attribute extraction

CLC Number: 

  • TP391
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