Label-Guided Dual-Attention Deep Neural Network Model

被引：0
|
作者：
Peng Z. [1 ]
Zhu X. [1 ]
Guo J. [2 ]
机构：
[1] College of Computer Science and Engineering, Chongqing University of Technology, Chongqing
[2] Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2022年 / 35卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Dual-Attention; Label Attention; Label Description Generation; Self-Attention; Sentiment Classification;
D O I：
10.16451/j.cnki.issn1003-6059.202202008
中图分类号：
学科分类号：
摘要：
Since the text information of labels is not included in some datasets, the semantic relationship between text words and labels cannot be explicitly calculated in the existing explicit interactive classification models. To solve this problem, a label-guided dual-attention deep neural network model is proposed in this paper. Firstly, an automatic category label description generation method based on inverse label frequency is proposed. According to the label description generation method, a specific label description for each label is generated. The generated specific label description is applied to explicitly calculate the semantic relationship between text words and labels. On the basis of the above, review text representation with contextual information is learned by a text encoder. A label-guided dual-attention network is proposed to learn the text representation based on self-attention and the text representation based on label attention, respectively. Then, an adaptive gating mechanism is employed to fuse two mentioned text representations and the final text representation is thus obtained. Finally, a two-layer feedforward neural network is utilized as a classifier for sentiment classification. Experiments on three publicly available real-world datasets show that the proposed model produces better classification performance. © 2022, Science Press. All right reserved.
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收藏
页码：175 / 184
页数：9
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