用户撰写文本中心理概念抽取与分类的可靠性分析
Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text
AI总结：
本文通过标注LoST数据集并分析预训练语言模型的注意力机制，研究用户文本中低自尊心理概念抽取的可靠性，发现模型应更关注LoST指标而非触发词和后果。
摘要：
社会NLP研究界近期见证了心理健康分析在计算层面的快速发展，其目标是构建负责任的AI模型，以应对语言使用与自我认知之间的复杂交互。此类负责任的AI模型有助于量化社交媒体上用户撰写文本中的心理概念。在超越低层级（分类）任务进行思考的基础上，我们将现有的二分类数据集推进到更高层级的任务，即通过解释的视角进行可靠性分析，并将其定位为安全措施之一。我们为LoST数据集进行了标注，以捕捉暗示Reddit用户帖子中存在低自尊的细微文本线索。我们进一步指出，为判断低自尊是否存在而开发的NLP模型更关注三类文本线索：（i）触发词（Trigger）：引发心理困扰的词语，（ii）LoST指标：强调低自尊的文本指示，以及（iii）后果（Consequences）：描述心理困扰后果的词语。我们实现了现有分类器，以检验预训练语言模型（PLMs）在特定领域、基于心理学的任务中的注意力机制。我们的发现表明，需要将PLMs的关注点从触发词和后果转向更全面的解释，在判断Reddit帖子中的低自尊时强调LoST指标。
英文摘要：
The social NLP research community witness a recent surge in the computational advancements of mental health analysis to build responsible AI models for a complex interplay between language use and self-perception. Such responsible AI models aid in quantifying the psychological concepts from user-penned texts on social media. On thinking beyond the low-level (classification) task, we advance the existing binary classification dataset, towards a higher-level task of reliability analysis through the lens of explanations, posing it as one of the safety measures. We annotate the LoST dataset to capture nuanced textual cues that suggest the presence of low self-esteem in the posts of Reddit users. We further state that the NLP models developed for determining the presence of low self-esteem, focus more on three types of textual cues: (i) Trigger: words that triggers mental disturbance, (ii) LoST indicators: text indicators emphasizing low self-esteem, and (iii) Consequences: words describing the consequences of mental disturbance. We implement existing classifiers to examine the attention mechanism in pre-trained language models (PLMs) for a domain-specific psychology-grounded task. Our findings suggest the need of shifting the focus of PLMs from Trigger and Consequences to a more comprehensive explanation, emphasizing LoST indicators while determining low self-esteem in Reddit posts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
