Abstract
Chinese medical named entity recognition (NER) task usually lacks sufficient annotation data, and it contains many medical professional terms and abbreviations, making the NER task more difficult. In addition, compared with English NER, Chinese NER is more challenging because it lacks standard feature symbols to determine named entity boundaries. Therefore, Chinese NER needs to perform word segmentation. In this paper, we are inspired by lexicon-based BERT and propose a novel method for Chinese medical NER task. Besides, We design a template-based strategy to enrich the words’ information and improve the model’s ability to distinguish medical professional terms and abbreviations. Our method enhances the word segmentation accuracy by introducing the external medical lexicon. To verify the effectiveness of our method, we carry out experiments on three medical datasets and our method improves them by 0.92%, 1.18% and 1.55% F1-score compared to baseline.
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References
Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a high-performance learning name-finder. arXiv preprint arXiv:cmp-lg/9803003 (1998)
Chen, X., et al.: Lightner: a lightweight generative framework with prompt-guided attention for low-resource NER. arXiv preprint arXiv:2109.00720 (2021)
Cui, L., Wu, Y., Liu, J., Yang, S., Zhang, Y.: Template-based named entity recognition using bart. arXiv preprint arXiv:2106.01760 (2021)
Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for chinese bert. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 3504–3514 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Forney, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)
Fukuda, K.J., Tsunoda, T., Tamura, A., Takagi, T., et al.: Toward information extraction: identifying protein names from biological papers. In: Pacific Symposium on Biocomputing, vol. 707, pp. 707–718. Citeseer (1998)
Gan, Z., et al.: Incorporate lexicon into self-training: a distantly supervised chinese medical NER. In: Wang, L., Feng, Y., Hong, Yu., He, R. (eds.) NLPCC 2021. LNCS (LNAI), vol. 13028, pp. 338–349. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88480-2_27
Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y.G., Huang, X.: CNN-based chinese NER with lexicon rethinking. In: IJCAI, pp. 4982–4988 (2019)
Gui, T., et al.: A lexicon-based graph neural network for Chinese NER. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1040–1050 (2019)
He, H., Sun, X.: F-score driven max margin neural network for named entity recognition in chinese social media. arXiv preprint arXiv:1611.04234 (2016)
Kuru, O., Can, O.A., Yuret, D.: Charner: character-level named entity recognition. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 911–921 (2016)
Li, X., Yan, H., Qiu, X., Huang, X.: FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6836–6842. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.611, https://aclanthology.org/2020.acl-main.611
Liu, W., Fu, X., Zhang, Y., Xiao, W.: Lexicon enhanced Chinese sequence labeling using BERT adapter. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5847–5858. Association for Computational Linguistics, Online 2021). https://doi.org/10.18653/v1/2021.acl-long.454, https://aclanthology.org/2021.acl-long.454
Liu, W., Xu, T., Xu, Q., Song, J., Zu, Y.: An encoding strategy based word-character LSTM for chinese NER. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2379–2389 (2019)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Ma, R., Peng, M., Zhang, Q., Huang, X.: Simplify the usage of lexicon in chinese NER. arXiv preprint arXiv:1908.05969 (2019)
Mccallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. Assoc. Comput. Linguist. 4, 188–191 (2003)
Rindflesch, T.C., Tanabe, L., Weinstein, J.N., Hunter, L.: Edgar: extraction of drugs, genes and relations from the biomedical literature. In: Biocomputing 2000, pp. 517–528. World Scientific (1999)
Sekine, S.: Description of the japanese NE system used for met-2. In: Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29-May 1, 1998 (1998)
Song, M., Yu, H., Han, W.S.: Developing a hybrid dictionary-based bio-entity recognition technique. BMC Med. Inform. Decis. Mak. 15(1), 1–8 (2015)
Sui, D., Chen, Y., Liu, K., Zhao, J., Liu, S.: Leverage lexical knowledge for chinese named entity recognition via collaborative graph network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3830–3840 (2019)
Takeuchi, K., Collier, N.: Use of support vector machines in extended named entity recognition. In: COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002) (2002), https://aclanthology.org/W02-2029
Tsai, R.T.H., Sung, C.L., Dai, H.J., Hung, H.C., Sung, T.Y., Hsu, W.L.: Nerbio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition. In: BMC bioinformatics, vol. 7, pp. 1–14. BioMed Central (2006)
Tsuruoka, Y., Tsujii, J.: Improving the performance of dictionary-based approaches in protein name recognition. J. Biomed. Inform. 37(6), 461–470 (2004)
Wu, Y., Fang, X., Li, J., Zhang, L., Chen, Z., Wang, Y.: A deep learning approach with conditional random field for automatic sleep stage scoring. In: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, pp. 901–906 (2021)
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1554–1564. Association for Computational Linguistics, Melbourne, Australia (2018). https://doi.org/10.18653/v1/P18-1144, https://aclanthology.org/P18-1144
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The research was supported by Natural Science Foundation of Fujian Province, PR China (2022J01120).
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Zhang, L. et al. (2022). Chinese Medical Named Entity Recognition Using External Knowledge. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_27
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