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Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention

机译:多头自我注意的不良药物反应提示信息的检测与提取

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This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.
机译:本文介绍了第四次健康应用社交媒体挖掘(SMM4H)研讨会的第一项和第二项共享任务的系统。我们使用语言模型来增强推特表示,并通过多头自注意来区分不同单词的重要性。另外,利用转移学习来弥补数据不足。我们的系统在两项任务上均取得了竞争优势,任务1的F1得分为F718,任务2的F1得分为0.653(重叠)/0.357(严格)。

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