用自然语言处理技术检测赌博与饮食障碍的早期征兆。
SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing
- 结合句子嵌入与多种语言特征分析文本
- 任务一F1达0.808，任务三排名第二
- 适合心理健康筛查与NLP应用研究者
本文介绍SINAI团队参与eRisk@CLEF 2022评测的情况。针对两项任务：任务一为病态赌博的早期征兆检测，任务三为饮食障碍症状严重程度评估。任务一采用基于Transformer的句子嵌入，融合体积、词汇多样性、复杂度指标和情绪评分等特征；任务三则利用上下文词嵌入进行文本相似性估计。在任务一中，团队以0.808的F1分数位列41个参赛方案中的第二名；任务三中，团队在3支参赛队伍中同样获得第二名。
原文摘要 · Abstract (English)
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, two of the proposed tasks have been addressed: i) Task 1 on the early detection of signs of pathological gambling, and ii) Task 3 on measuring the severity of the signs of eating disorders. The approach presented in Task 1 is based on the use of sentence embeddings from Transformers with features related to volumetry, lexical diversity, complexity metrics, and emotion-related scores, while the approach for Task 3 is based on text similarity estimation using contextualized word embeddings from Transformers. In Task 1, our team has been ranked in second position, with an F1 score of 0.808, out of 41 participant submissions. In Task 3, our team also placed second out of a total of 3 participating teams.
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