The CHIP 2023 Shared Task 6: Chinese Diabetes Question Classification

Author:

Li Shunhao,Qian Xiaobo,Tan Kehui,Wang Maojie,Hao Tianyong

Publisher

Springer Nature Singapore

Reference20 articles.

1. Hao, T., Li, X., He, Y., Wang, F.L., Qu, Y.: Recent progress in leveraging deep learning methods for question answering. Neural Comput. Appl. 34(4), 2765–2783 (2022)

2. Hao, T., Xie, W., Wu, Q., Weng, H., Qu, Y.: Leveraging question target word features through semantic relation expansion for answer type classification. Knowl.-Based Syst. 133, 43–52 (2017)

3. Liu, Y., Hao, T., Liu, H., Mu, Y., Weng, H., Wang, F.L.: OdeBERT: one-stage deep supervised early-exiting BERT for fast inference in user intent classification. ACM Trans. on Asian Low-Resour. Lang. Inf. Process. 22(5), 1–18 (2023)

4. Xie, W., Ding, R., Yan, J., et al.: A mobile-based question-answering and early warning system for assisting diabetes management. Wirel. Commun. Mob. Comput. 2018, 1–14 (2018)

5. Qian, X., Xie, W., Long, S., Lan, M., Mu, Y., Hao, T.: The construction of question taxonomy and an annotated Chinese corpus for diabetes question classification. In: Proceedings of The Chinese National Conference on Computational Linguistics, pp.395–405 (2022)

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