International Journal of Computational Linguistics Research
Neural Network Ensemble Approach for the Chinese Clinical Named Entity Recognition
Ling Luo, Nan Li, Shuaichi Li, Zhihao Yang, Hongfei Lin College of Computer Science and Technology Dalian University of Technology, Dalian China 116024
Abstract:
[1] Uzuner, Ö., Solti, I., Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American Medical Informatics Association, 2010, 17 (5) 514-518. [2] Sun, W., Rumshisky, A., Uzuner, O. (2013). Evaluating temporal relations in clinical text: 2012 i2b2 challenge. Journal of the American Medical Informatics Association 2013, 20 (5) 806-813. [3] Bethard, S., Savova, G., Chen, W-T., Derczynski, L., Pustejovsky, J., Verhagen, M. (2016). Semeval-2016 task 12: Clinical tempeval. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016): 2016. 1052-1062. [4] Collobert. R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P (2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 2011, 12(Aug), 2493-2537. [5] Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C. (2016). Neural architectures for named entity recognition. arXiv preprint arXiv:160301360 2016. [6] Ma, X., Hovy, E. (2016). End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:160301354. [7] He, J., Wang, H. (2008). Chinese named entity recognition and word segmentation based on character. In: Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing: 2008. [8] Li, H., Hagiwara, M., Li, Q., Ji, H. (2014). Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese. In: LREC: 2014. 2532-2536. [9] Zhang, Y., Yang, J. (2018). Chinese NER Using Lattice LSTM. arXiv preprint arXiv:180502023 2018.
Keywords: Entity Recognition, Chinese Clinical Text, Neural Network, Ensemble Neural Network Ensemble Approach for the Chinese Clinical Named Entity Recognition
References:[1] Uzuner, Ö., Solti, I., Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American Medical Informatics Association, 2010, 17 (5) 514-518.
[2] Sun, W., Rumshisky, A., Uzuner, O. (2013). Evaluating temporal relations in clinical text: 2012 i2b2 challenge. Journal of the American Medical Informatics Association 2013, 20 (5) 806-813.
[3] Bethard, S., Savova, G., Chen, W-T., Derczynski, L., Pustejovsky, J., Verhagen, M. (2016). Semeval-2016 task 12: Clinical tempeval. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016): 2016. 1052-1062.
[4] Collobert. R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P (2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 2011, 12(Aug), 2493-2537.
[5] Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C. (2016). Neural architectures for named entity recognition. arXiv preprint arXiv:160301360 2016.
[6] Ma, X., Hovy, E. (2016). End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:160301354.
[7] He, J., Wang, H. (2008). Chinese named entity recognition and word segmentation based on character. In: Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing: 2008.
[8] Li, H., Hagiwara, M., Li, Q., Ji, H. (2014). Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese. In: LREC: 2014. 2532-2536.
[9] Zhang, Y., Yang, J. (2018). Chinese NER Using Lattice LSTM. arXiv preprint arXiv:180502023 2018.
