@inproceedings{liu-etal-2021-deep,
title = "Deep Attention Diffusion Graph Neural Networks for Text Classification",
author = "Liu, Yonghao and
Guan, Renchu and
Giunchiglia, Fausto and
Liang, Yanchun and
Feng, Xiaoyue",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.642",
doi = "10.18653/v1/2021.emnlp-main.642",
pages = "8142--8152",
abstract = "Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.",
}
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<abstract>Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.</abstract>
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%0 Conference Proceedings
%T Deep Attention Diffusion Graph Neural Networks for Text Classification
%A Liu, Yonghao
%A Guan, Renchu
%A Giunchiglia, Fausto
%A Liang, Yanchun
%A Feng, Xiaoyue
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-deep
%X Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.
%R 10.18653/v1/2021.emnlp-main.642
%U https://aclanthology.org/2021.emnlp-main.642
%U https://doi.org/10.18653/v1/2021.emnlp-main.642
%P 8142-8152
Markdown (Informal)
[Deep Attention Diffusion Graph Neural Networks for Text Classification](https://aclanthology.org/2021.emnlp-main.642) (Liu et al., EMNLP 2021)
ACL
- Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, and Xiaoyue Feng. 2021. Deep Attention Diffusion Graph Neural Networks for Text Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8142–8152, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.