Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yogatama, D., et al.: Generative and discriminative text classification with recurrent neural networks (2017). arXiv preprint, arXiv:1703.01898
Tu, Z., et al.: Modeling coverage for neural machine translation (2016). arXiv preprint, arXiv:1601.04811
Cao, Z., et al.: Improving multi-document summarization via text classification. In: AAAI (2017)
Conneau, A., et al.: Very deep convolutional networks for natural language processing (2016). arXiv preprint, arXiv:1606.01781
Joshi, A.J. Porikli, A.J., Papanikolopoulos, N.: Multi-class active learning for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2372–2379 (2009)
Tur, G., Hakkani, D.: Combining active and semi-supervised learning for spoken language understanding. Speech Commun. 45(2), 171–186 (2005)
Settles, B.: Active learning literature survey. Univ. Wisconsin, Madison 52(55–66), 11 (2010)
MacKay, David J.C.: Information-based objective functions for active data selection. Neural Comput. 4(4), 590–604 (1992)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Active learning with Gaussian processes for object categorization. In: ICCV (2007)
Roy, N., McCallum, A.: Toward optimal active learning through monte carlo estimation of error reduction. In: ICML (2001)
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning (2016)
Li, X., Guo, Y.: Active learning with multi-label SVM classification. In: IJCAI (2013)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM (1992)
McCallumzy, A.K., Nigamy, K.: Employing EM and pool-based active learning for text classification. In: Proceedings of the International Conference on Machine Learning (ICML), pp.359–367 (1998). Citeseer
Wang, K., et al.: Cost-effective active learning for deep image classification. IEEE Trans. Circ. Syst. Video Technol. 27(12), 2591–2600 (2017)
Dasgupta, S.: Coarse sample complexity bounds for active learning. In: Advances in Neural Information Processing Systems (2006)
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Fu, L., Yin, Z., Liu, Y., Zhang, J. (2018). Convolution Neural Network with Active Learning for Information Extraction of Enterprise Announcements. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)
