Distributed Representations of Emotion Categories in Emotion Space
Xiangyu Wang | Chengqing Zong
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories. The existing studies working on emotion detection usually focus on how to improve the performance of model prediction, in which emotions are represented with one-hot vectors. However, emotion relations are ignored in one-hot representations. In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. Furthermore, based on the soft labels predicted by the pre-trained neural network model, we derive a simple and effective algorithm. Experiments have validated that the proposed representations in emotion space can express emotion relations much better than word vectors in semantic space.
A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
Yan Chen | Zhoujun Li | Liqiang Nie | Xia Hu | Xiangyu Wang | Tat-Seng Chua | Xiaoming Zhang
Proceedings of COLING 2012
- Chengqing Zong 1
- Yan Chen 1
- Zhoujun Li 1
- Liqiang Nie 1
- Xia Hu 1
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