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2021 | OriginalPaper | Buchkapitel

A Residual Dynamic Graph Convolutional Network for Multi-label Text Classification

verfasst von : Bingquan Wang, Jie Liu, Shaowei Chen, Xiao Ling, Shanpeng Wang, Wenzheng Zhang, Liyi Chen, Jiaxin Zhang

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Recent studies often utilize the Graph Convolutional Network (GCN) to learn label dependencies features for the multi-label text classification (MLTC) task. However, constructing the static label graph according to the pairwise co-occurrence from training datasets may degrade the generalizability of the model. In addition, GCN-based methods suffer from the problem of over-smoothing. To this end, we propose a Residual Dynamic Graph Convolutional Network Model (RDGCN) (https://github.com/ilove-Moretz/RDGCN.git) which adopts a label attention mechanism to learn the label-specific representations and then constructs a dynamic label graph for each given instance. Furthermore, we devise a residual connection to alleviate the over-smoothing problem. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results show the superiority of our proposed model.

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Titel: A Residual Dynamic Graph Convolutional Network for Multi-label Text Classification
verfasst von: Bingquan Wang
Jie Liu
Shaowei Chen
Xiao Ling
Shanpeng Wang
Wenzheng Zhang
Liyi Chen
Jiaxin Zhang
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-88479-6

Electronic ISBN: 978-3-030-88480-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88480-2_53

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