A Weighted Online Recurrent Extreme Learning Machine-Based Method for Disease Names Recognition
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-90633-7_60
Reference23 articles.
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2. Zheng, W., et al.: An effective neural model extracting document level chemical-induced disease relations from biomedical literature. J. Biomed. Inform. 83, 1–9 (2018). https://doi.org/10.1016/j.jbi.2018.05.001
3. Xu, K., Zhou, Z., Gong, T., Hao, T., Liu, W.: SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields. BMC Med. Inf. Decis. Making 18(S5) (2018). https://doi.org/10.1186/s12911-018-0690-y
4. Li, J., Sun, Y., et al.: BioCreative v CDR task corpus: a resource for chemical disease relation extraction. Database 2016, baw068 (2016). https://doi.org/10.1093/database/baw068
5. Advances in Intelligent Systems and Computing;E El-allaly,2019
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