Abstract
Numerical understanding relies on some content understanding techniques, which can be based on rules, entity extraction, and machine reading comprehension. Traditional methods often require a large number of regular expressions or a large number of data annotations, and often do not have a deep understanding of numerical values, lacking the ability to distinguish similar numerical values. In this paper, we propose a few-shot learning framework for numerical understanding tasks in Chinese medical texts, and through dynamic negative sampling of the training data, the model’s ability to discriminate similar numerical values is enhanced. We use patient text data provided by 13 hospitals in Beijing to conduct experiments. The results show that our newly proposed method is superior to training the baseline pretrained language model directly, the EM increases by 38% and the F1 increases by 27.59%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. In: ACM symposium on Applied Computing, pp. 1637–1641 (2009)
Li, J., Ming, Z., et al.: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Archit. 57(9), 840–849 (2011)
Qiu, M., Chen, Z., Ming, Z., Qin, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2014)
Niu, J., Gao, Y., et al.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. JPDC 72(12), 1565–1575 (2012)
Qiu, M., Xue, C., Shao, Z., Zhuge, Q., Liu, M., Sha, E.-M.: Efficent algorithm of energy minimization for heterogeneous wireless sensor network. In: Sha, E., Han, S.-K., Xu, C.-Z., Kim, M.-H., Yang, L.T., Xiao, B. (eds.) EUC 2006. LNCS, vol. 4096, pp. 25–34. Springer, Heidelberg (2006). https://doi.org/10.1007/11802167_5
Qiu, H., Dong, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J. 8(13), 10327–10335 (2020)
Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. ITS 22(7), 4560–4569 (2020)
Li, Y., Gai, K., et al.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. Multimedia Comput. Commun. Appl. 12(4s), 1–18 (2016)
Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity (2016)
Hu, F., Lakdawala, S., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf. Technol. Biomed. 13(4), 656–663 (2009)
Harper, C., Cox, J., Kohler, C., et al.: SemEval-2021 task 8: MeasEval–extracting counts and measurements and their related contexts. In: 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 306–316, August 2021
Therien, B., Bagherzadeh, P., Bergler, S.: CLaC-BP at SemEval-2021 task 8: SciBERT plus rules for MeasEval. In: SemEval-2021, pp. 410–415, August 2021
Foppiano, L., Romary, L., et al.: Automatic identification and normalization of physical measurements in scientific literature. In: ACM Symposium on Document Engineering, pp. 1–4 (2019)
Gangwar, A., Jain, S., Sourav, S., Modi, A.: Counts@IITK at SemEval-2021 task 8: SciBERT based entity and semantic relation extraction for scientific data. arXiv preprint arXiv:2104.01364 (2021)
Kohler, C., Daniel Jr, R.: What’s in a measurement? Using GPT-3 on SemEval 2021 task 8–MeasEval. arXiv preprint arXiv:2106.14720 (2021)
Davletov, A., Gordeev, D., Arefyev, N., Davletov, E.: LIORI at SemEval-2021 task 8: ask transformer for measurements. In: 15th SemEval-2021, pp. 1249–1254, August 2021
Avram, A.M., Zaharia, G.E., Cercel, D.C., Dascalu, M.: UPB at SemEval-2021 task 8: extracting semantic information on measurements as multi-turn question answering. arXiv preprint arXiv:2104.04549 (2021)
Hovy, E., Marcus, M., Palmer, M., et al.: OntoNotes: the 90% solution. In: Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pp. 57–60, June 2006
Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know? Trans. Assoc. Comput. Linguist. 8, 423–438 (2020)
Roberts, A., Raffel, C., Shazeer, N.: How much knowledge can you pack into the parameters of a language model? arXiv preprint arXiv:2002.08910 (2020)
Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., Liu, Q.: ERNIE: enhanced language representation with informative entities. arXiv preprint arXiv:1905.07129 (2019)
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: unanswerable questions for SQuAD. arXiv preprint arXiv:1806.03822 (2018)
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)
Joshi, M., Chen, D., Liu, Y., et al.: SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020)
Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for Chinese BERT. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 3504–3514 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, X., Hu, W., Liu, X., Chen, Y., Shao, W., Sun, L. (2023). Few-Shot Learning for Medical Numerical Understanding Based on Machine Reading Comprehension. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-031-28124-2_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28123-5
Online ISBN: 978-3-031-28124-2
eBook Packages: Computer ScienceComputer Science (R0)