面向情感学习为面向层面的情感分类.
Zhongquan Jian1, Jiajian Li1, Meihong Wang2
1Institute of Artificial Intelligence, Xiamen University, Xiamen, 361005, Fujian, China.
概括
通过从相关的句子中学习,AspLearn优化了方面情感语义,提高了方面级情感分类 (ALSC) 的性能. 这种方法增强了特征生成,并提高了大型语言模型 (LLM) 的情绪识别能力.
科学领域:
- 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:
- 视角级情感分类 (ALSC) 是情感分析 (SA) 中的一个关键任务.
- 当前的方法往往孤立地分析句子,错过了关键的句子间关系,以获得方面情感.
- 这种局限性阻碍了对方面情感语义的全面理解.
研究的目的:
- 引入AspLearn,这是ALSC的一种新的方面学习方法.
- 为了优化方面情感语义,并产生强大的方面特定的句子特征.
- 通过利用句子间关系来提高ALSC模型的性能.
主要方法:
- AspLearn使用了面向意识的对比学习 (AspCL).
- AspCL从相关样本中挖掘与方面相关的知识,以改进方面情感语义.
- 该方法整合了这些学到的知识,以改善ALSC的句子特征生成.
主要成果:
- AspLearn 在三个基准数据集中展示了卓越的方面学习能力.
- 该方法在笔记本电脑,餐厅和Twitter数据集上取得了显著的Macro F1得分改进,而不是现有的最先进的结果.
- 实验显示使用DeBERTa作为骨干模型显著提高了性能.
结论:
- AspLearn有效地优化了方面情感语义,并提高了ALSC的性能.
- 该方法从句子间关系中学习的能力提供了更强大的特定方面特征.
- 通过相关的示范检索,AspLearn还显示了改善大型语言模型 (LLM) 情绪识别的潜力.
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