广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (3): 40-48.doi: 10.16088/j.issn.1001-6600.2021091503

• 研究论文 • 上一篇    下一篇

基于双向语言模型的社交媒体药物不良反应识别

李正光1, 陈恒1*, 林鸿飞2   

  1. 1.大连外国语大学 语言智能研究中心, 辽宁 大连 116044;
    2.大连理工大学 计算机科学与技术学院, 辽宁 大连 116024
  • 收稿日期:2021-09-15 修回日期:2021-12-20 出版日期:2022-05-25 发布日期:2022-05-27
  • 通讯作者: 陈恒(1982—), 男, 安徽太和人, 大连外国语大学副教授, 博士。E-mail: chenheng@dlufl.edu.cn
  • 基金资助:
    国家自然科学基金(61806038); 辽宁省高等学校创新人才项目(WR2019005); 辽宁省教育厅科学研究经费项目(2020JYT03); 教育部人文社科项目(18YJCZH2018);大连外国语大学科研基金(2021XJYB16,2021XJYB19)

Identification of Adverse Drug Reaction on Social Media Using Bi-directional Language Model

LI Zhengguang1, CHEN Heng1*, LIN Hongfei2   

  1. 1. Research Center for Language Intelligence, Dalian University of Foreign Language, Dalian Liaoning 116044, China;
    2. School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116024, China
  • Received:2021-09-15 Revised:2021-12-20 Online:2022-05-25 Published:2022-05-27

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摘要： 与服药相关的社交文本中隐藏着更具时效和更广泛的药物不良反应信息,但是从相对短小、稀疏的社交短文本中提取药物不良反应非常困难。基于此,本文提出一种双向语言预训练模型和注意力机制相结合的神经网络识别方法。该方法利用双向字符级语言预训练模型提取特定字符级特征,而且在提取药物不良反应的同时,通过注意力机制捕获局部和全局语义上下文信息。此外,为了提高该方法的效率,将字符级特征与词级特征相结合,并采用词级预训练和字符级预训练模型代替协同训练。在PSB 2016社交媒体挖掘共享任务2中的实验结果表明,字符特征在形态学上有助于区分药物不良反应,而注意力机制通过捕获局部和全局语义信息提高了对药物不良反应的识别性能,宏平均F1值为82.2%。

关键词: 药物不良反应, 社交媒体, 双向语言模型, 注意力机制, 预训练模型

Abstract: More time-effective and wider adverse drug reactions are concealed in tweets related to feelings of taking medication. However, it is difficult to extract adverse drug reaction (ADR) from these tweets due to relatively shortness and sparseness of tweets. Therefore, a neural network model is proposed in this paper, which employes the pretrained bidirectional language model and attention mechanism to identify ADR. Firstly, specific character-level features are extracted via a pretrained bidirectional character-level neural language model. Secondly, the attention mechanism is used to capture local and global semantic contexts while extracting ADRs. Thirdly, to improve the efficiency of the proposed method, Character-level features are combined with word-level features. Finally, co-training is replaced with the pretrained of the whole-word level and fine-tuned pretrained character embeddings. These optimizations contribute to improving the performance of identification. The proposed model achieves better performance on the PSB 2016 Social Media Mining Sharing Task Workshop-Task 2: ADR Extraction, obtaining the F1-scores of 82.2% on official datasets. Character features are useful for distinguishing ADR and non-ADR in morphology. In addition, attention mechanism improves the performance of identifying ADR due to capturing local and global semantic contexts.

Key words: adverse drug reaction, social media, bi-directional language model, attention mechanism, pretrained model

中图分类号: 

  • TP391.1
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