A hybrid approach for named entity recognition in Chinese electronic medical record
Tóm tắt
Từ khóa
Tài liệu tham khảo
Zong Q. Statistical natural language processing. Beijing: Tsinghua University Press; 2008.
Li L, JIN L, Jiang Z, et al. Biomedical named entity recognition based on extended recurrent neural networks. IEEE Int Conf Bioinformatics Biomed. 2015:649–52.
tmChem. A high performance approach for chemical entity recognition and normalization. J Cheminformatics. 2015;7(S1):S3.
Siwei L, Liheng X, Kang L, Jun Z. Recurrent convolutional neural networks for text classification. Assoc Adv Artificial Intelligence (AAAI). 2015;2267-2273.
Ling L, Zhihao Y, Pei Y, et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinfromatics. 2017;34(8).
Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. Int Conf Learn Representations (ICLR). 2015.
Mnih V, Heess N, Graves A, Kavukcuoglu K. Recurrent models of visual attention. Int Conf Neural Inf Process Syst (NIPS). 2014;2204-2212.
Chen L, Chen B, Ren YF, Ji DH. Long short-term memory RNN for biomedical named entity recognition. Bioinformatics. 2017;18(462).
Xiang X. Conditional Random Field based Chinese Named Entity Recognition. Xiamen: Xiamen University; 2006.
Zhang Z, Ren F. A comparative study of features on CRF-based Chinese named entity recognition. National Conference on Information Retrieval and Content Security (NCIRCS). 2008;111-117.
Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch. J Mach Learn Res. 2011:2493–537.
Huang Z, Xu W, Bidirectional YK. LSTM-CRF models for sequence tagging. Computer Science. 2015.
Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition. Annual conference of the North American chapter of the Association for Computational Linguistics (NAACL). In: 260—270; 2016.
Ma XZ, Eduard H. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. Ann Meet Assoc Comput Linguist (ACL). 2016.
Dong C, Zhang J, Zong C, et al. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. International conference on computer processing of oriental languages. Springer International Publishing, vol. 2017;221—230:72.
Chen T, Xu RF, He YL, et al. Improving sentiment analysis via sentence type classification usint BiLSTM-CRF and CNN. Experts systems with applications. In: 260-270; 2016.
Drug, http://app1.sfda.gov.cn/datasearch/face3/dir.html , 8th July, 2018.
CCKS 2018 named entity recognition of Chinese electronic medical record, https://www.biendata.com/competition/CCKS2018_1/ , 12th August, 2018.