| 摘要: |
| 揭示情绪之间的关系是认知心理学的一项重要基础研究. 从自然语言处理的角度来说, 探讨情绪之间的关系的关键在于得到合适的情绪类别的嵌入式表示. 最近, 在情感空间中获得一个可以表征情绪关系的类别表示已经引起了一些关注. 然而, 现有的情绪类别嵌入方法存在以下几个缺点. 比如固定维度, 情绪类别表示的维度依赖于所选定的数据集. 为了取得一个更好的情绪类别表示, 引入监督对比学习的表示方法. 在之前的监督对比学习方法中, 样本之间的相似性取决于样本所标注的标签的相似性. 为了更好地反映出不同情绪类别之间的复杂关系, 进一步提出部分相似的监督对比学习表示方法, 认为不同情绪类别(比如情绪anger和annoyance)的样本之间也可能是部分相似的. 最后, 组织一系列实验来验证所提方法以及其他5个基准方法在表述情绪类别之间关系的能力. 实验结果表明, 所提方法取得了理想的情绪类别表示结果. |
| 关键词: 情感分析 情绪表示 情绪空间 情绪类别 |
| DOI：10.13328/j.cnki.jos.006999 |
| 分类号:TP18 |
| 基金项目: |
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| Supervised Contrastive Learning for Text Emotion Category Representations |
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WANG Xiang-Yu1, ZONG Cheng-Qing1,2
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1.National Laboratory of Pattern Recognition (Institute of Automation, Chinese Academy of Sciences), Beijing 100190, China;2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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| Abstract: |
| Revealing the complex relations among emotions is an important fundamental study in cognitive psychology. From the perspective of natural language processing, the key to exploring the relations among emotions lies in the embedded representation of emotional categories. Recently, there has been some interest in obtaining a category representation in the emotion space that can characterize emotion relations. However, the existing methods for emotion category representations have several drawbacks. For example, fixed dimensionality, the dimensionality of the emotion category representation, depends on the selected dataset. In order to obtain better representations for the emotion categories, this study introduces a supervised contrastive learning representation method. In the previous supervised contrastive learning, the similarity between samples depends on the similarity of the annotated labels of the samples. In order to better reflect the complex relations among different emotion categories, the study further proposes a partially similar supervised contrastive learning representation method, which suggests that samples of different emotion categories (e.g., anger and annoyance) may also be partially similar to each other. Finally, the study organizes a series of experiments to verify the ability of the proposed method and the other five benchmark methods in representing the relationship between emotion categories. The experimental results show that the proposed method achieves satisfactory results for the emotion category representations. |
| Key words: sentiment analysis emotion representation emotion space emotion category |
