.png){kind=icon}
返回
A knowledge-augmented neural network model for sarcasm detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2023.103521.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic sarcasm detection from text is one important research task in text mining and natural language processing and has attracted extensive attention from researchers. Most approaches focus on designing various models and features according to the original text, without making use of the knowledge and information from external knowledge source such as Wikipedia, which is publicly available. In this paper, we investigate a knowledge-augmented neural network model that leverages the contextual information of the original text from external knowledge source, for sarcasm detection. We first extract the context from external knowledge source for the original text. Then, the original text and its context are fed sequentially into the embedding layer and encoding layer, to automatically learn high-level semantic representation. Finally, we use the softmax layer to output the classification probability. Based on the Semeval-2018 Task 3 dataset, results show that our proposed model gives 82.79% F1 score, outperforming the existing models and strong neural baselines with significant margins. Experimental analysis also indicates that the contextual information is highly important for sarcasm detection.
Keyword:
Sarcasm detection
Irony detection
Neural networks
Contextual information
Deep learning
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks基于集成迁移学习的加权卷积神经网络多模态情感分析
PREAI
分享收藏Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM基于多头注意力的双向LSTM讽刺检测.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
Lead-rubber shielding effect on radiation dose to the gonads from a bilateral hand X-ray examinationRadiography
RadiographyIF0
PREAI
分享收藏
查看更多内容