文本情绪原因识别作为一个新型的研究方向在文本情绪分析领域占据重要地位。该文结合卷积神经网络,提出了一种基于集成卷积神经网络的情绪原因识别方法。该方法通过词向量、卷积、池化等操作充分融合了句子的语义信息,利用多个CNN集成降低数据不平衡性对情绪原因识别的影响,克服了传统情绪原因识别方法的繁琐规则制定、特征抽取、特征空间降维等过程。实验结果表明,该文的方法在情绪原因识别方面取得了较好的效果,对于情绪归因的方法研究具有一定的指导作用。
Abstract
Emotion cause detection, as a new and challenging issue, plays an important role in the field of text emotion analysis. In this paper, we present a new emotion cause detection method based on ensembled convolution neural networks. This proposed method combines the semantic information by operations such as word embedding, convolution, pooling, and it uses multiple CNN integrations to reduce the impact of data imbalance on emotional reasons detection and relieve tedious processes, such as rule-making, feature extraction, feature dimension reduction and so on. The experiments results show the proposed method has a good performance.
关键词
情绪原因识别 /
E-CNN /
卷积 /
池化
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Key words
emotion cause detection /
E-CNN /
convolution /
pooling
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参考文献
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基金
国家自然科学基金(61632011,61573231,61672331,61432011,61603229);山西省自然科学资助项目(201601D021076)
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