@inproceedings{ding-etal-2020-less,
title = "Be More with Less: Hypergraph Attention Networks for Inductive Text Classification",
author = "Ding, Kaize and
Wang, Jianling and
Li, Jundong and
Li, Dingcheng and
Liu, Huan",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.399",
doi = "10.18653/v1/2020.emnlp-main.399",
pages = "4927--4936",
abstract = "Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model {--} hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.",
}
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<abstract>Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model – hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.</abstract>
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%0 Conference Proceedings
%T Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
%A Ding, Kaize
%A Wang, Jianling
%A Li, Jundong
%A Li, Dingcheng
%A Liu, Huan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ding-etal-2020-less
%X Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model – hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.
%R 10.18653/v1/2020.emnlp-main.399
%U https://aclanthology.org/2020.emnlp-main.399
%U https://doi.org/10.18653/v1/2020.emnlp-main.399
%P 4927-4936
Markdown (Informal)
[Be More with Less: Hypergraph Attention Networks for Inductive Text Classification](https://aclanthology.org/2020.emnlp-main.399) (Ding et al., EMNLP 2020)
ACL