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

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.