Abstract
Named Entity Recognition (NER) in the medical field targets to extract names of disease, surgery, and the organ location from medical texts, which is considered as the fundamental work for medical robots and intelligent diagnosis systems. It is very challenging to recognize the named entities in Chinese medical texts, because (a) one single Chinese medical named entity is usually expressed with more characters/words than other languages, i.e. 3.2 words and 7.3 characters in average; (b) different types of medical named entities are usually nested together. To address the above issue, this paper presents a neural framework that is constructed by two modules: a pre-trained module to distinguish each individual entity from the nested expressions, while a modified Bi-LSTM module to effectively identify long entities. We conducted the experiments based on the CCKS2019 dataset, our proposed method can identify the medical entity in Chinese, especially for those nested entities embodied in long expressions, and 95.83% was achieved in terms of F1-score, and 18.64% improvement was achieved compared to the baseline models.
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Acknowledgements
This research is supported by the Natural Science Foundation of China (61976066, 61502115, U1636103), the Fundamental Research Fund for the Central Universities (3262019T29), the Joint funding (SKX182010023, 2019GA35) and Students’ Academic Training Program of UIR (3262019SXK15).
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Zhao, Z., Zhou, Z., Xing, W., Wu, J., Chang, Y., Li, B. (2020). A Neural Framework for Chinese Medical Named Entity Recognition. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science(), vol 12401. Springer, Cham. https://doi.org/10.1007/978-3-030-59605-7_6
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