在用户撰写的文本中提取和分类心理概念的可靠性分析
Muskan Garg1, Msvpj Sathvik2, Shaina Raza3
1Mayo Clinic, Rochester, MN, USA.
概括
这项研究通过关注社交媒体文本中低自尊指标来增强心理健康分析的AI. 负责任的AI模型应该优先考虑这些特定的线索,以获得更好的准确性.
科学领域:
- 计算语言学计算语言学
- 心理健康分析精神健康分析
- 负责任的人工智能开发
背景情况:
- 社交媒体文本分析正在推进心理健康应用.
- 负责任的人工智能模型从用户生成的内容中量化心理概念.
- 现有的模型往往侧重于低级分类任务.
研究的目的:
- 在心理健康分析中超越二元分类.
- 通过解释引入可靠性分析作为安全措施.
- 为了注释LoST数据集对低自尊感的细微文字线索.
主要方法:
- 用特定的文本线索 (触发器,LoST指标,后果) 对LoST数据集进行注释.
- 实施现有的分类器来检查预训练语言模型 (PLM) 中的注意力机制.
- 通过使用特定领域的数据,对基于心理学的任务分析PLM.
主要成果:
- 对于低自尊的NLP模型往往侧重于触发和后果.
- 在PLM中的注意力机制显示了对不同文本线索的不同关注.
- 为了更好的表现,需要将重点转向LoST指标.
结论:
- 目前用于低自尊检测的NLP模型需要改进.
- 在PLM中优先考虑LoST指标可以提高心理健康分析的准确性.
- 加强对特定文本线索的关注,导致在心理健康方面更负责任的AI.
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