@inproceedings{zhang-etal-2020-every,
title = "Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks",
author = "Zhang, Yufeng and
Yu, Xueli and
Cui, Zeyu and
Wu, Shu and
Wen, Zhongzhen and
Wang, Liang",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.31",
doi = "10.18653/v1/2020.acl-main.31",
pages = "334--339",
abstract = "Text classification is fundamental in natural language processing (NLP) and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. Therefore in this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structure, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.",
}
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<abstract>Text classification is fundamental in natural language processing (NLP) and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. Therefore in this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structure, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.</abstract>
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%0 Conference Proceedings
%T Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
%A Zhang, Yufeng
%A Yu, Xueli
%A Cui, Zeyu
%A Wu, Shu
%A Wen, Zhongzhen
%A Wang, Liang
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-every
%X Text classification is fundamental in natural language processing (NLP) and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. Therefore in this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structure, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.
%R 10.18653/v1/2020.acl-main.31
%U https://aclanthology.org/2020.acl-main.31
%U https://doi.org/10.18653/v1/2020.acl-main.31
%P 334-339
Markdown (Informal)
[Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://aclanthology.org/2020.acl-main.31) (Zhang et al., ACL 2020)
ACL