推信中的基于性别的语言差异
Sunyang Fu1, Darren Q Calley2, Veronica A Rasmussen2
1Department of AI and Informatics, Mayo Clinic, Rochester, MN.
概括
在医学教育推信 (LOR) 中的性别刻板印象使用自然语言处理 (NLP) 进行了分析. NLP模型发现了微妙的语言偏见,揭示了男性和女性作家如何描述申请人的差异.
科学领域:
- 医疗教育 医学教育
- 自然语言处理自然语言处理.
- 性别研究是性别研究.
背景情况:
- 推信 (LOR) 在生物医学教育中对于候选人评估至关重要.
- 地方自治区的主观性质可以引入基于性别的语言偏见.
- 积极识别和减轻性别刻板印象对于平等至关重要.
研究的目的:
- 开发和评估自然语言处理 (NLP) 方法,以检测物理治疗住院LOR中的性别偏见.
- 从LOR中提取心理语言和主题特征,以识别偏见的语言.
- 为了比较男性和女性作家和申请人之间的语言使用模式.
主要方法:
- 开发了四种NLP方法:子语言分析,基于字典,基于规则和深度学习.
- 模型在3个梅奥诊所物理治疗住院计划中的LORs上进行了评估.
- 在主题类别提取中,MedTaggerIE和双向编码器从变压器表示 (BERT) 模型显示了中等至高的性能.
主要成果:
- 男性作家更经常使用"智力"和"特殊"等术语.
- 男性申请人与"力量"和"人际交往技巧"等术语有关.
- 在男性和女性作家如何表达怀疑,动机和建议方面,观察到显著的差异.
结论:
- NLP有效地识别了LORs中的性别偏见语言.
- 在LORs中,特定的词汇选择和主题表达方式在性别之间存在差异.
- 发现需要在研究的背景下仔细解释,需要进一步研究.
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