Convolution Neural Network with Active Learning for Information Extraction of Enterprise Announcements

  • Lei Fu
  • Zhaoxia Yin
  • Yi Liu
  • Jun ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


We propose using convolution neural network (CNN) with active learning for information extraction of enterprise announcements. The training process of supervised deep learning model usually requires a large amount of training data with high-quality reference samples. Human production of such samples is tedious, and since inter-labeler agreement is low, very unreliable. Active learning helps assuage this problem by automatically selecting a small amount of unlabeled samples for humans to hand correct. Active learning chooses a selective set of samples to be labeled. Then the CNN is trained on the labeled data iteratively, until the expected experimental effect is achieved. We propose three sample selection methods based on certainty criterion. We also establish an enterprise announcements dataset for experiments, which contains 10410 samples totally. Our experiment results show that the amount of labeled data needed for a given extraction accuracy can be reduced by more than 45.79% compared to that without active learning.


Text classification Active learning Convolutional neural networks Enterprise announcements 



This work is partially supported by Shenzhen Science & Research projects (No: JCYJ20160331104524983) and Key Technologies Research & Development Program of Shenzhen (No: JSGG20160229121006579). We thank the reviewers for the insightful comments.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of EducationAnhui UniversityHefeiPeople’s Republic of China
  2. 2.PKU Shenzhen InstituteShenzhenChina
  3. 3.Shenzhen Securities Information, Co., Ltd.ShenzhenChina

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