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Chinese Clinical Named Entity Recognition Based on Stroke-Level and Radical-Level Features

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Smart Computing and Communication (SmartCom 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12608))

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Abstract

Clinical Named Entity Recognition (CNER) is an important step for mining clini-cal text. Aiming at the problem of insufficient representation of potential Chinese features, we propose the Chinese clinical named entity recognition model based on stroke level and radical level features. The model leverages Bidirectional Long Short-term Memory (BiLSTM) neural network to extract the internal semantic in-formation of Chinese characters (i.e., strokes and radicals). Our method can not only capture the dependence of the internal strokes of Chinese characters, but also enhance the semantic representation of Chinese characters, thereby improving the entity recognition ability of the model. Experimental results show that the accuracy of the model on the CCKS-2017 task 2 benchmark data set reaches 93.66%, and the F1 score reaches 94.70%. Comp ared with the basic BiLSTM-CRF mod-el, the precision of model is increased by 3.38%, the recall is increased by 1.05% and F1 value is increased by 1.91%.

F. Zhou, X. Han—These authors contributed equally

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References

  1. Lossio-Ventura, J.A., et al.: Towards an obesity-cancer knowledge base: biomedical entity identification and relation detection. In: IEEE International Conference on Bioinformatics & Biomedicine IEEE (2016)

    Google Scholar 

  2. Habibi, M., Weber, L., Neves, M., Wiegandt, D.L., Leser, U.: Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics 33(14), i37–i48 (2017)

    Article  Google Scholar 

  3. Wang, Y., Ananiadou, S., Tsujii, J.: Improving clinical named entity recognition in Chinese using the graphical and phonetic feature. BMC Med. Inform. Decis. Mak. 19(7), 1–7 (2019)

    Article  Google Scholar 

  4. Cai, X., Dong, S., Jinlong, H.: A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records. BMC Med. Infor. Decis. Mak. 19(2), 101–109 (2019)

    Google Scholar 

  5. Ji, B., Liu, R., Li, S., Jie, Yu., Qingbo, W., Tan, Y., Jiaju, W.: A hybrid approach for named entity recognition in Chinese electronic medical record. BMC Med. Inform. Decis. Mak. 19(2), 149–158 (2019)

    Google Scholar 

  6. Coden, A., et al.: Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. J. Biomed. Inform. 42(5), 937–949 (2009)

    Article  Google Scholar 

  7. Song, M., Yu, H., Han, W.S.: Developing a hybrid dictionary-based bio-entity recognition technique. BMC Med. Inform. Decis. Mak. 15(1), S9 (2015)

    Article  Google Scholar 

  8. Friedman, C., Alderson, P.O., Austin, J.H.M., Cimino, J.J., Johnson, S.B.: A general natural-language text processor for clinical radiology. J. Am. Med. Inform. Assoc. 1(2), 161–174 (1994)

    Article  Google Scholar 

  9. Feng, Y., Ying-Ying, C., Gen-Gui, Z., Hao-Min, L., Ying, L.: Intelligent recognition of named entity in electronic medical records. Chinese J. Biomed. Eng. 30, 256–262 (2011)

    Google Scholar 

  10. Zeng, Q.T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S.N., Lazarus, R.: Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med. Inform. Decis. Mak. 6(1), 30 (2006)

    Article  Google Scholar 

  11. Savova, G.K., et al.: Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17(5), 507–513 (2010)

    Article  Google Scholar 

  12. McCallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons (2003)

    Google Scholar 

  13. McCallum, A., Freitag, D., Pereira, F.C.N.: Maximum entropy Markov models for information extraction and segmentation. Icml 17(2000), 591–598 (2000)

    Google Scholar 

  14. Zhang, J., Shen, D., Zhou, G., Jian, S., Tan, C.-L.: Enhancing HMM-based biomedical named entity recognition by studying special phenomena. J. Biomed. Inform. 37(6), 411–422 (2004)

    Article  Google Scholar 

  15. Xia, Y., Wang, Q.: Clinical named entity recognition: ECUST in the CCKS-2017 shared task 2. CEUR Workshop Proc. 2017, 43–48 (1976)

    Google Scholar 

  16. Ouyang, E., Li, Y., Jin, L., Li, Z., Zhang, X.: Exploring n-gram character presentation in bidirectional RNN-CRF for Chinese clinical named entity recognition. CEUR Workshop Proc. 2017, 37–42 (1976)

    Google Scholar 

  17. Li, Z., Zhang, Q., Liu, Y., Feng, D., Huang, Z.: Recurrent neural networks with specialized word embedding for Chinese clinical named entity recognition. CEUR Workshop Proc. 2017, 55–60 (1976)

    Google Scholar 

  18. Wang, Q., Zhou, Y., Ruan, T., Gao, D., Xia, Y., He, P.: Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition. J. Biomed. Inform. 92, 103133 (2019)

    Article  Google Scholar 

  19. Guohua, W., Tang, G., Wang, Z., Zhang, Z., Wang, Z.: An attention-based BiLSTM-CRF model for chinese clinic named entity recognition. IEEE Access 7, 113942–113949 (2019)

    Article  Google Scholar 

  20. Yin, M., Mou, C., Xiong, K., Ren, J.: Chinese clinical named entity recognition with radical-level feature and self-attention mechanism. J. Biomed. Inform. 98, 103289 (2019)

    Article  Google Scholar 

  21. Hu, J., Shi, X., Liu, Z., Wang, X., Chen, Q., Tang, B.: HITSZ CNER: a hybrid system for entity recognition from Chinese clinical text. CEUR workshop proc. 1976 (2017)

    Google Scholar 

  22. Qiu, J., Wang, Q., Zhou, Y., Ruan, T., Gao, J.: Fast and accurate recognition of Chinese clinical named entities with residual dilated convolutions. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) vol. 2018, pp. 935–942 (2018)

    Google Scholar 

  23. Zhao, S., Cai, Z., Chen, H., Wang, Y., Liu, F., Liu, A.: Adversarial training based lattice LSTM for Chinese clinical named entity recognition. J. Biomed. Inform. 99, 103290 (2019)

    Article  Google Scholar 

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Acknowledgments

The research was supported by the National Science Foundation of China under grant No. 61572225 and 61472049, the Foundation of Jilin Provincial Education Department under grant under grant No. JJKH20190724KJ, the Jilin Province Science & Technology Department Foundation under grant No.20190302071GX and 20200201164JC, the Development and Reform Commission Foundation of Jilin province under grant No. 2019C053-11.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Feng Zhou & Yong Li

  2. College of Information Science and Technology, Jinan University, Guangzhou, 510632, China

    Xuming Han

  3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150006, China

    Qiaoming Liu

  4. School of Management, Jilin University, Changchun, 130022, China

    Mingyang Li

Authors
  1. Feng Zhou
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  2. Xuming Han
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  3. Qiaoming Liu
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  4. Mingyang Li
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  5. Yong Li
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Corresponding author

Correspondence to Qiaoming Liu .

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Editors and Affiliations

  1. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Zhou, F., Han, X., Liu, Q., Li, M., Li, Y. (2021). Chinese Clinical Named Entity Recognition Based on Stroke-Level and Radical-Level Features. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2020. Lecture Notes in Computer Science(), vol 12608. Springer, Cham. https://doi.org/10.1007/978-3-030-74717-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-74717-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74716-9

  • Online ISBN: 978-3-030-74717-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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