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

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

研究成果: 書貢獻/報告類型會議論文篇章

摘要

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.

原文英語
主出版物標題ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
編輯Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
發行者The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面257-264
頁數8
ISBN(電子)9789869576949
出版狀態已發佈 - 2021
事件33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, 臺灣
持續時間: 2021 10月 152021 10月 16

出版系列

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

會議

會議33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
國家/地區臺灣
城市Taoyuan
期間2021/10/152021/10/16

ASJC Scopus subject areas

  • 語言與語言學
  • 語言和語言學
  • 言語和聽力

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