论文标题
使用Roberta进行不良药物事件识别的Sui Generis QA方法
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification
论文作者
论文摘要
从生物医学文献和其他文本数据中提取不良药物事件是监测药物安全的重要组成部分，这引起了许多医疗保健研究人员的关注。使用双向长期内存网络（BI-LSTM），现有作品更围绕实体关系提取，这无法获得最佳的功能表示。在本文中，我们介绍了一个问题回答框架，该框架利用了罗伯塔（Roberta）的鲁棒性，掩盖和动态关注能力，通过一种适应性的技术，并试图克服上述限制。我们的模型的表现优于先前的工作9.53％的F1得分。
Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score.
