从Twitter上提取COVID-19事件通过提取式问题解答与连续提示
Yuhang Jiang1, Ramakanth Kavuluru2
1Department of Computer Science, University of Kentucky, Lexington, KY, USA.
Studies in health technology and informatics
|January 25, 2024
概括
社交媒体分析可以通过分析推特来增强COVID-19跟踪. 这项研究改进了使用语言模型和持续提示的公共卫生洞察力的事件提取.
科学领域:
- 计算语言学计算语言学
- 公共卫生信息学 公共卫生信息学
- 社交媒体分析.
背景情况:
- 由于COVID-19的流行,需要新的方法来跟踪公共卫生趋势.
- 传统调查在实时数据捕获方面存在局限性.
- 社交媒体提供了一个庞大的,动态的公共情绪和事件披露的来源.
研究的目的:
- 改进从社交媒体数据中提取COVID-19相关事件.
- 使用新的Twitter数据集对事件提取性能进行基准测试.
- 评估语言模型的有效性,并持续提示此任务.
主要方法:
- 将事件提取作为一个提取式问答问题.
- 利用语言模型持续提示的最新进展.
- 将方法应用于2020年W-NUT共享任务COVID-19数据集.
主要成果:
- 在共享任务测试数据集上实现了超过5%的绝对微平均F1分数改进.
- 与之前的最佳结果相比,在所有COVID-19活动时间段中表现出卓越的表现.
- 一项废除研究证实了持续提示对表现的重大影响.
结论:
- 具有持续提示的语言模型提供了一个强大的方法,可以从社交媒体中提取COVID-19事件.
- 这种方法提高了医疗机构监测和应对疫情的能力.
- 社交媒体分析,当与先进的NLP技术相结合时,可以显著增加公共卫生监测.
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