自然语言处理的理论驱动分析 用生成语言建模测量思维障碍
Isaac Fradkin1, Matthew M Nour2, Raymond J Dolan3
1Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.
自然语言处理 (NLP) 可以通过使用深度学习模型模拟形式思维障碍 (FTD) 来帮助精神病学研究. 这项研究揭示了NLP指标如何检测特定的FTD损伤,提供新的分析工具.
科学领域:
- 计算语言学计算语言学
- 精神病诊断诊断精神病诊断 精神病诊断 精神病诊断
- 医疗保健中的人工智能
背景情况:
- 自然语言处理 (NLP) 显示了推进精神病学研究和临床实践的潜力.
- NLP已经在自动检测语音失调方面取得了成功,这是正式思维障碍 (FTD) 的一个关键特征.
- 在理解NLP指标与FTD理论模型的关系方面存在差距.
研究的目的:
- 调查NLP指标与FTD理论账户之间的关系.
- 使用深度生成语言模型来模拟类似FTD的叙述.
- 扰乱计算参数,以实例化FTD的基于理论的机制.
主要方法:
- 模拟的FTD类叙述使用生成式预训变压器-2.
- 操纵单词选择的随机性和模型内存跨度.
- 评估了脱轨和接触性的NLP措施.
主要成果:
- 增加的随机性和有限的记忆范围都增加了句子之间的语义距离.
- 单词之间的语义距离有所变化:因随机性而增加,因有限的记忆力而减少.
- 一个触点测量被有限的记忆跨度独特地预测,显示出非单调的效应.
- 句子的意思是编码方法显著影响了性能.
结论:
- 一种基于模拟的方法被验证为精神病学NLP的假设生成.
- 这种方法有助于在精神病学中对NLP标记物的机械分析.
- 提供了一个Python教程,以促进这种方法的采用.
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