基于对抗性迁移学习的药品不良反应实体识别研究

Identifying Named Entities of Adverse Drug Reaction with Adversarial Transfer Learning

【目的】为解决在线健康社区中实体表述不规范和边界不显著的问题,提出一种基于对抗性迁移学习的药品不良反应实体识别模型ATL-BCA。【方法】通过Word2Vec生成融合在线医疗健康领域外部语义特征向量;基于迁移学习思想采用共享和私有BiLSTM分别抽取实体识别和分词任务的共享边界信息及私有信息;利用多头注意力机制捕捉句子整体依赖性,并使用对抗训练过滤分词任务的私有信息以消除冗余特征对实体识别任务的影响;最后,借助条件随机场约束预测标签序列结果。【结果】在自构建药品不良反应数据集上进行实验,引入对抗性迁移学习的ATL-BCA模型实体识别F1值达到91.35%,较主流模型Word2VecBiLSTM-CRF和BERT-BiLSTM-CRF分别提升5.28和2.98个百分点。【局限】仅选用“三九健康药物网”作为实验数据源,且数据集规模较小。【结论】ATL-BCA模型不仅可以充分利用实体识别和分词任务共享边界信息,而且能够过滤分词任务私有特征,从而有效提升在线健康社区中药品不良反应实体识别效果。

[Objective]This paper proposes an entity recognition model for adverse drug reactions based on adversarial transfer learning,ATL-BCA,aiming to address the problem of non-standard entity representations and insignificant boundaries in online health communities.[Methods]Firstly,we generated the external semantic feature vectors fused with the online medical domain knowledge with Word2Vec.Secondly,based on the transfer learning,we utilized the shared and private BiLSTM to extract the shared boundary information and private features for entity recognition and word segmentation tasks.Next,we used the multi-head attention mechanism to capture the overall sentence dependency and used adversarial training to filter the private information of the word segmentation task.This helped us eliminate the influence of redundant features on the entity recognition task.Finally,we predicted the label sequence results with the help of CRF constraints.[Results]We used a selfconstructed social media adverse drug reaction dataset to examine the proposed model with.The F1 value of the new model reached 91.35%,which is 5.28%and 2.98%higher than Word2Vec-BiLSTM-CRF and BERTBiLSTM-CRF.[Limitations]We only retrieved the experimental data from Sanjiu Health&Medicine Site,the scale of the constructed dataset is relatively small.[Conclusions]The ATL-BCA model fully utilizes the shared boundary information between entity recognition and word segmentation tasks.It also filters the private features of the word segmentation tasks,effectively improving the entity recognition performance of adverse drug reactions in online health communities.