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

Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
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引用次数: 5

Abstract

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.
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本文描述了我们的系统用于第四届健康应用社交媒体挖掘(SMM4H)研讨会的第一和第二共享任务。我们利用语言模型增强tweet的表示,并利用多头自注意区分不同单词的重要性。此外,利用迁移学习来弥补数据的不足。我们的系统在两个任务上都取得了竞争结果,任务1的f1得分为0.5718,任务2的f1得分为0.653(重叠)/ 0.357(严格)。
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Approaching SMM4H with Merged Models and Multi-task Learning BIGODM System in the Social Media Mining for Health Applications Shared Task 2019 HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets Lexical Normalization of User-Generated Medical Text Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
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