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    <abstract>“社交媒体是药物不良反应(ADR)检测的重要途径之一。本文提出一个基于社交媒体的药物不良反应检测模型DMFE,以全面捕捉患者对药物使用的反馈信息。与传统的文本检测相比,社交媒体数据中通常会有语法不规范与单词拼写错误的问题。本文提取出社交媒体数据的抽象语义表示(AMR)使用图注意力网络(GAT)学习抽象语义特征提高模型对语义信息的理解,使用字符级卷积神经网络(charCNN)捕获字符特征以减少单词拼写错误带来的影响。此外,本文使用提示学习的方法融入荍荥荤荄荒荁药物不良反应领域关键词进一步增强模型对领域知识的理解能力。经实验评估,本文模型DMFE在CADEC、TwiMed两个数据集上F1值与基线模型相比取得最优效果。”</abstract>
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%0 Conference Proceedings
%T 面向社交媒体多特征增强的药物不良反应检测(Adverse drug reaction detection with multi-feature enhancement for social media)
%A Hao, Li
%A Yunzhi, Qiu
%A Hongfei, Lin
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F hao-etal-2024-mian
%X “社交媒体是药物不良反应(ADR)检测的重要途径之一。本文提出一个基于社交媒体的药物不良反应检测模型DMFE,以全面捕捉患者对药物使用的反馈信息。与传统的文本检测相比,社交媒体数据中通常会有语法不规范与单词拼写错误的问题。本文提取出社交媒体数据的抽象语义表示(AMR)使用图注意力网络(GAT)学习抽象语义特征提高模型对语义信息的理解,使用字符级卷积神经网络(charCNN)捕获字符特征以减少单词拼写错误带来的影响。此外,本文使用提示学习的方法融入荍荥荤荄荒荁药物不良反应领域关键词进一步增强模型对领域知识的理解能力。经实验评估,本文模型DMFE在CADEC、TwiMed两个数据集上F1值与基线模型相比取得最优效果。”
%U https://aclanthology.org/2024.ccl-1.40/
%P 515-525

Markdown (Informal)

[面向社交媒体多特征增强的药物不良反应检测(Adverse drug reaction detection with multi-feature enhancement for social media)](https://aclanthology.org/2024.ccl-1.40/) (Hao et al., CCL 2024)

• 面向社交媒体多特征增强的药物不良反应检测(Adverse drug reaction detection with multi-feature enhancement for social media) (Hao et al., CCL 2024)

ACL

• Li Hao, Qiu Yunzhi, and Lin Hongfei. 2024. 面向社交媒体多特征增强的药物不良反应检测(Adverse drug reaction detection with multi-feature enhancement for social media). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 515–525, Taiyuan, China. Chinese Information Processing Society of China.