Prior work has shown that analyzing the use of first-person singular pronouns can provide insight into individuals' mental status, especially depression symptom severity. These findings were generated by counting frequencies of first-person singular pronouns in text data. However, counting doesn't capture how these pronouns are used. Recent advances in neural language modeling have leveraged methods generating contextual embeddings. In this study, we sought to utilize the embeddings of first-person pronouns obtained from contextualized language representation models to capture ways these pronouns are used, to analyze mental status. De-identified text messages sent during online psychotherapy with weekly assessment of depression severity were used for evaluation. Results indicate the advantage of contextualized first-person pronoun embeddings over standard classification token embeddings and frequency-based pronoun analysis results in predicting depression symptom severity. This suggests contextual representations of first-person pronouns can enhance the predictive utility of language used by people with depression symptoms.

翻译：先前研究表明，分析第一人称单数代词的使用可揭示个体心理状态，特别是抑郁症状的严重程度。这些发现源于对文本数据中第一人称单数代词出现频率的统计。然而，简单计数无法捕捉这些代词的使用方式。近期神经语言建模的进展已可利用生成上下文嵌入的方法进行实现。本研究尝试通过从上下文语言表示模型中提取第一人称代词的嵌入表示，来捕捉这些代词的使用方式，进而分析心理状态。研究采用在线心理治疗过程中去身份识别的短信文本及每周抑郁严重程度评估数据进行验证。结果表明，相较于标准分类标记嵌入和基于频率的代词分析方法，上下文化的第一人称代词嵌入在预测抑郁症状严重程度方面更具优势。这提示第一人称代词的上下文表示能够提升抑郁症状患者语言使用的预测效能。