1. Akbik, A., Blythe, D., Vollgraf, R.: Contextual string embeddings for sequence labeling. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1638–1649. Association for Computational Linguistics, Santa Fe, New Mexico, USA (Aug 2018). https://aclanthology.org/C18-1139
2. Alfattni, G., Belousov, M., Peek, N., Nenadic, G.: Extracting drug names and associated attributes from discharge summaries: text mining study. JMIR Med. Inform. 9(5), e24678 (2021)
3. Caliskan, D., et al.: First steps to evaluate an NLP tool’s medication extraction accuracy from discharge letters. Stud. Health Technol. Inform. 278, 224–230 (2021)
4. Denny, J.C., Spickard, A., 3rd., Johnson, K.B., Peterson, N.B., Peterson, J.F., Miller, R.A.: Evaluation of a method to identify and categorize section headers in clinical documents. J. Am. Med. Inform. Assoc. 16(6), 806–815 (2009)
5. 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)