迈向文本处理管道以识别与不良药物事件相关的推文:密歇根大学@ SMM4H 2019任务1

V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas, D. Yu, Amy M. Austin, N. Bhomia, S. Chan, S. Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, D. Teng, H. Tseng, Kim Vuong, Stephanie Zimmerman
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引用次数: 5

摘要

我们参与了社交媒体健康应用挖掘(SMM4H) 2019年共享任务的任务1,该任务涉及检测推文中提到的药物不良事件(ADEs)。我们的方法依赖于推文的文本处理管道,以及训练传统的机器学习和深度学习模型。我们提交的运行执行高于任务的平均水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.
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