用大模型与Transformer集成提升医疗文本分类效果
1024m at SMM4H 2024: Tasks 3, 5 & 6 -- Ensembles of Transformers and Large Language Models for Medical Text Classification
- 采用Transformer与大模型集成方法处理医疗文本
- 在任务3、5、6中均取得优于单模型的表现
- 适合医疗健康领域文本分析研究者参考
社交媒体是用户报告健康状况及其受各种因素影响的重要数据来源。本文针对SMM4H'24的三个任务，提出多种基于Transformer和大型语言模型及其集成的方法：任务3为分类作者关于自然与户外空间对其心理健康影响的文本；任务5为二分类用户发布的关于孩子患有哮喘、自闭症、多动症及语言障碍的推文；任务6为二分类用户自我报告的年龄信息。实验展示了各模型在不同任务中的性能表现、优势与局限性。
原文摘要 · Abstract (English)
Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).
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