Be More with Less: Hypergraph Attention Networks for Inductive Text Classification

• URL: http://arxiv.org/abs/2011.00387v1
• Date: Sun, 1 Nov 2020 00:21:59 GMT
• Title: Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
• Authors: Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu
• Abstract summary: 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 unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
• Score: 56.98218530073927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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