用Transformer和LSTM检测网络言论中的赌博倾向，提升早期预警能力
SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
- 结合预训练模型与LSTM，通过数据清洗和平衡提升检测效果
- 在49支队伍中排名第七，召回率与早期检测指标最优
- 适合关注心理健康监测与风险行为预警的研究者
本文描述了SINAI团队参与eRisk@CLEF 2023评测任务的情况。针对路径性赌博的早期迹象识别任务（Task 2），团队采用基于Transformer架构的预训练模型，配合全面的数据预处理与数据平衡技术，并将长短期记忆网络（LSTM）与Transformer自动模型融合。在该任务中，团队取得第7名，F1得分为0.126，超越其他48个参赛方案，在召回率及早期检测相关指标上表现最佳。
原文摘要 · Abstract (English)
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has been ranked in seventh position, with an F1 score of 0.126, out of 49 participant submissions and achieves the highest values in recall metrics and metrics related to early detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。