Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks

Hao Chuan Kao; Man Chen Hung; Lung Hao Lee; Yuen Hsien Tseng

Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks

Hao Chuan Kao, Man Chen Hung, Lung Hao Lee, Yuen Hsien Tseng

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.

Original language	English
Title of host publication	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
Editors	Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	257-264
Number of pages	8
ISBN (Electronic)	9789869576949
State	Published - 2021
Event	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan Duration: 15 Oct 2021 → 16 Oct 2021

Publication series

Name	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

Conference

Conference	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Country/Territory	Taiwan
City	Taoyuan
Period	15/10/21 → 16/10/21

Keywords

Humor recognition
Hypergraph neural networks
Multi-label classification

Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(2/3)
Lee, L.-H. (PI)
1/05/20 → 30/04/21
Project: Research

Cite this

Kao, H. C., Hung, M. C., Lee, L. H., & Tseng, Y. H. (2021). Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. In L.-H. Lee, C.-H. Chang, & K.-Y. Chen (Eds.), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (pp. 257-264). (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

Kao, Hao Chuan ; Hung, Man Chen ; Lee, Lung Hao et al. / Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. editor / Lung-Hao Lee ; Chia-Hui Chang ; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. pp. 257-264 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

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title = "Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks",

abstract = "We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.",

keywords = "Humor recognition, Hypergraph neural networks, Multi-label classification",

author = "Kao, {Hao Chuan} and Hung, {Man Chen} and Lee, {Lung Hao} and Tseng, {Yuen Hsien}",

note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",

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series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",

publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",

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editor = "Lung-Hao Lee and Chia-Hui Chang and Kuan-Yu Chen",

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Kao, HC, Hung, MC, Lee, LH & Tseng, YH 2021, Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. in L-H Lee, C-H Chang & K-Y Chen (eds), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 257-264, 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021, Taoyuan, Taiwan, 15/10/21.

Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. / Kao, Hao Chuan; Hung, Man Chen; Lee, Lung Hao et al.
ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ed. / Lung-Hao Lee; Chia-Hui Chang; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. p. 257-264 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Kao, Hao Chuan

AU - Hung, Man Chen

AU - Lee, Lung Hao

AU - Tseng, Yuen Hsien

PY - 2021

Y1 - 2021

N2 - We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.

AB - We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.

KW - Humor recognition

KW - Hypergraph neural networks

KW - Multi-label classification

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M3 - 會議論文篇章

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BT - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

A2 - Lee, Lung-Hao

A2 - Chang, Chia-Hui

A2 - Chen, Kuan-Yu

PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

T2 - 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

Y2 - 15 October 2021 through 16 October 2021

ER -

Kao HC, Hung MC, Lee LH, Tseng YH. Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks. In Lee LH, Chang CH, Chen KY, editors, ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2021. p. 257-264. (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks

Abstract

Publication series

Conference

Keywords

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Projects

Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(2/3)

Cite this