Abstract
Extracting named entity especially organization name from Chinese management case texts is a challenging task due to the lack of labeled data and difficulty in identifying diversified forms of entity names. In this paper, a semi-supervised learning method based on bidirectional long short-term memory and conditional random field (BI-LSTM-CRF) model was proposed. This method has a bootstrapped framework which automatically learns complex text features from a small number of seed sets with the BI-LSTM-CRF model and then updates the seed sets after evaluating the recognition results according to the rule base. It stops iterating when the precision of the model comes to convergence. The experimental results show that the accuracy of the proposed method reaches 89.2%, which outperforms other semi-supervised learning models.
This is a preview of subscription content, log in via an institution.
References
Wenchen, Guo, Rong, Dai, Shaosheng, Sun: Research on management case study in China: actuality and prospect. Chin. J. Manag. 13(5), 664–670 (2016)
Ning, Wang, Ruifang, Ge, Chunfa, Yuan, et al.: Company name identification in Chinese financial domain. J. Chin. Inf. Process. 16(2), 1–6 (2002)
Saha, S.K., Sarkar, S., Mitra, P.: Feature selection techniques for maximum entropy based biomedical named entity recognition. J. Biomed. Inform. 42(5), 905–911 (2009)
Lee, K., Hwang, Y., Kim, S., et al.: Biomedical named entity recognition using two-phase model based on SVMs. J. Biomed. Inform. 37(6), 436–447 (2004)
Hongkui, Yu., Huaping, Zhang, Qun, Liu, et al.: Chinese named entity identification using cascaded hidden Markov model. J. Commun.cations 27(2), 87–94 (2006)
Junsheng, Zhou, Xinyu, Dai, Cunyan, Yin, et al.: Automatic recognition of chinese organization name based on cascaded conditional random fields. Acta Electronica Sinica 34(5), 804–809 (2006)
Bengio, Y., Ducharme, R., Vincent, P., et al.: A neural probabilistic language model. J. Mach. Learn. Res. 3(6), 1137–1155 (2003)
Li, L., Jin, L., Jiang, Z., et al.: Biomedical named entity recognition based on extended recurrent neural networks. In: IEEE International Conference on Bioinfonnatics and Biomedicine, pp. 649–652 (2015)
Kadari, R., Zhang, Y.U., Zhang, W., et al.: CCG supertagging with bidirectional long short-term memory networks. Nat. Lang. Eng. 24(1), 77–90 (2018)
Gridach, M.: Character-level neural network for biomedical named entity recognition. J. Biomed. Inform. 70, 85–91 (2017)
Etzioni, O., Cafarella, M., Downey, D., et al.: Unsupervised named-entity extraction from the Web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)
Dang, V.B., Aizawa, A.: Multi-class named entity recognition via bootstrapping with dependency tree-based patterns. In: 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 76–87 (2008)
Stevenson, M., Greenwood, M.A.: A semantic approach to IE pattern induction. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL’05), pp. 379–386 (2005)
Acknowledgements
The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China under Grant No. 71871018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, S., Zhang, X., Zhan, X. (2020). Named Entity Recognition for Chinese Management Case Texts. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-9406-5_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9405-8
Online ISBN: 978-981-13-9406-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)