带有多头自关注的药品不良反应提及推文的检测与提取

Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI:10.18653/v1/W19-3214

Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang

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引用次数: 5

摘要

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)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

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