基于情感膨胀门控CNN的情感-原因对提取

Extracting Emotion-Cause Pairs Based on Emotional Dilation Gated CNN

【目的】针对情感-原因对抽取这一情感分析任务,提出情感膨胀门控CNN(EDGCNN)模型。【方法】首先使用情感判别模型CNN找出情感句,然后将情感句编码输入到融入情感特征的EDGCNN模型,找出相应的情感原因,得到情感-原因对,并在实验数据集进行情感原因关键字标注。【结果】召回率和F1值分别达到了63.52%和60.45%,召回率优于已有方法最好结果,F1值与已有方法最优性能相当,而且能从更细粒度实现情感-原因对抽取。【局限】情感-原因对语料规模较小,有待进一步扩充完善。【结论】EDGCNN模型能够从文本中更好地抽取情感-原因对。

[Objective]This paper proposes an Emotional Dilation Gated CNN(EDGCNN)model,aiming to extract emotion-cause pairs for sentiment analysis.[Methods]First,we used the emotional discriminant model to identify sentiment sentences.Then,we input coding for these sentences to the EDGCNN model and located corresponding reasons.Finally,we tagged keywords of reasons generated from the experimental dataset.[Results]The new model’s recall and F1 values reached 63.52%and 60.45%respectively on the training dataset,which were better or very similiar to the existing ones The proposed model also extracted emotion-cause pairs at finergranularity level.[Limitations]The experimental corpus size was small.[Conclusions]The proposed model can extract emotion-cause pairs effectively.