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

摘要：

　　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.

关键词： Text classification Active learning Convolutional neural networks Enterprise announcements

作者: Lei Fu Zhaoxia Yin Yi Liu Jun Zhang

作者单位: Key Laboratory of Intelligent Computing and Signal Processing,Ministry of Education,Anhui University Key Laboratory of Intelligent Computing and Signal Processing,Ministry of Education,Anhui University PKU Shenzhen Institute,Shenzhen,China Shenzhen Securities Information,Co.,Ltd.,Shenzhen,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 330-339

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）