在MOOC中学习者生成的评论的认识情感识别的情感-语义-意识双重对比学习
概括
这项研究引入了一种情感-语义-意识双重对比学习 (ES-DCL) 方法,以改善大规模开放在线课程 (MOOC) 评论中认识论情感的识别. 新方法增强了特征表示,以便更好地指导学习者.
科学领域:
- 教育技术的教育技术
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 在大规模开放在线课程 (MOOC) 审查中识别学习者的认识情绪对于适应式的教师指导至关重要.
- 现有的方法通常通过仅分析语义或情感文本信息来使用不足的特征表示.
- 模两可的特征分布阻碍了对某些认知情感类别的准确区分.
研究的目的:
- 在MOOC评论中开发一种用于细粒度认识情感识别的新方法.
- 解决现有方法中特征表示和情感类别模糊性的局限性.
- 提高认识情感识别模型的准确性和辨别力.
主要方法:
- 提出了一种情感语义意识的双重对比学习 (ES-DCL) 方法.
- 从两个不同的视图中提取了互补的情感语义特征,以充分表示.
- 制定了标签和特征对比损失,结合领域专家知识,以改善特征分布和解决类别异构.
主要成果:
- 在四个MOOC数据集中,ES-DCL方法在认识情绪识别方面表现得更好.
- ES-DCL显著超过了11个基线模型,包括最先进的深度学习方法.
- 该方法学习了与现有方法相比,更具歧视性的句子表示.
结论:
- 拟议的ES-DCL方法有效地提高了MOOC审查中的认识情绪识别.
- 这种方法为理解学习者的情绪和提供有针对性的支持提供了更强大的解决方案.
- 通过创建更容易区分的表征来进行细粒度的情感分析,ES-DCL在这个领域取得了进展.
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