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DeBERTa-BiLSTM:一种多标签的分类模型,用于对抑郁情绪的分类
Abhijit Sarkar1, Amit Majumder1
1CSE, NIT Jamshedpur, Adityapur, Jamshedpur, 831014 Jharkhand India.
Cognitive neurodynamics
|February 13, 2026
概括
这项研究引入了DeBERTa-BiLSTM模型用于多标签抑郁情绪检测,在准确性和效率方面超过其他架构. 该模型有效地从文本中识别出各种抑郁情绪,推进心理健康分析.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 心理健康信息学心理健康信息学
背景情况:
- 多标签的抑郁情绪分类是复杂的,因为情绪重叠和细微的语言线索.
- 现有的方法难以处理长距离的依赖关系,难以区分主动/被动抑郁信号.
研究的目的:
- 为多标签抑郁情绪检测开发一个有效和计算效率高的框架.
- 通过使用先进的NLP模型,从文本中准确识别明确的活跃和潜伏的被动抑郁情绪.
主要方法:
- 评估了基于变压器和混合架构,包括带有BiLSTM的BERT,RoBERTa,T5,BART和DeBERTa.
- 拟议的DeBERTa-BiLSTM模型整合了解散的自我注意力和BiLSTM用于上下文和顺序学习.
- 利用了DepressionEmo数据集与八个临床相关的抑郁情绪.
主要成果:
- 该DeBERTa-BiLSTM模型实现了卓越的性能,F1-微的0.83和F1-宏的0.80.
- 证明了对频繁和少数情绪标签的强大检测,微精度为0.81和微回忆为0.85.
- 与seq2seq BART相比,展示了较好的推断效率,减少了每个样本的时间.
结论:
- DeBERTa-BiLSTM模型为准确和高效的多标签抑郁情绪检测提供了一个有希望的方法.
- 需要在更广泛的心理健康领域和更大的数据集进行进一步的验证.
- 该模型的性能突显了集成高级深度学习架构用于心理健康文本分析的潜力.
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