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

芳香一点

Abduct

CBC471

一条卷发

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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

epidermis

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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裂隙

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.

dry-eye

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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