PDF(Pubmed)
Abstract：
In the last decade, an increasing number of users have started reporting adverse drug events (ADEs) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use natural language processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question. Negations and speculations are pervasive phenomena in natural language and can severely hamper the ability of an automated system to discriminate between factual and non-factual statements in text. In this article, we take into consideration four state-of-the-art systems for ADE detection on social media texts. We introduce SNAX, a benchmark to test their performance against samples containing negated and speculated ADEs, showing their fragility against these phenomena. We then introduce two possible strategies to increase the robustness of these models, showing that both of them bring significant increases in performance, lowering the number of spurious entities predicted by the models by 60% for negation and 80% for speculations.
摘要：
在过去的十年里，越来越多的用户开始在社交媒体平台上报告药物不良事件(ADE)，博客,健康论坛。鉴于大量报告，药物警戒专注于使用自然语言处理（NLP）技术快速检查这些大型文本集合的方法，检测药物相关不良反应的提及，以引发医学调查。然而,尽管人们对这项任务和NLP的进步越来越感兴趣，面对否定和猜测等语言现象，这些模型的鲁棒性是一个悬而未决的研究问题。否定和猜测是自然语言中普遍存在的现象，会严重阻碍自动化系统区分文本中的事实陈述和非事实陈述的能力。在这篇文章中,我们考虑了四个最先进的系统，用于在社交媒体文本上检测ADE。我们介绍SAX,一个基准，用于测试它们对含有否定和推测的ADE的样品的性能，显示了他们对这些现象的脆弱性。然后，我们引入两种可能的策略来增加这些模型的稳健性，表明它们都带来了性能的显着提高，将模型预测的虚假实体数量减少60%的否定和80%的猜测。
