Titlebook: Health Information Processing; 8th China Conference Buzhou Tang,Qingcai Chen,Haitian Wang Conference proceedings 2023 The Editor(s) (if app

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1865-0929 ou, China from August 26–28, 2022..The 14 full papers presented in this volume were carefully reviewed and selected from a total of 35 submissions. The papers in the volume are organised according to the following topical headings: healthcare natural language processing;healthcare data mining and ap

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Huiwen Wu,Kanghui Zhang,Fan Wang,Jianhua Liu,Wang Zhao,Haiqing Xu,Long Luderstand the behaviour of parameters better. In this chapter we state the problems rigorously and discuss those results that do not use algebraic-geometric codes. We shall return to asymptotic problems in Chapter 3.4, since asymptotic results are the best to demonstrate the power of algebraic-geometric methods.

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A Biomedical Named Entity Recognition Framework with?Multi-granularity Prompt Tuning tasks, which effectively reduces the model’s dependence on annotated data. To evaluate the overall performance of Prompt-BioNER, we conduct extensive experiments on 3 datasets. Experimental results demonstrate that BioNER outperforms the the-state-of-the-arts methods, and it can achieve good performance under low resource conditions.

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An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at ..

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