Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks
Hao-Chuan Kao | Man-Chen Hung | Lung-Hao Lee | Yuen-Hsien Tseng
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
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.
Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees
Lung-Hao Lee | Po-Han Chen | Hao-Chuan Kao | Ting-Chun Hung | Po-Lei Lee | Kuo-Kai Shyu
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.
- Lung-Hao Lee 2
- Po-Han Chen 1
- Ting-Chun Hung 1
- Po-Lei Lee 1
- Kuo-Kai Shyu 1
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