3 citations · 8 across the 6 of their papers we have counts for
6 papers
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
Feng Xie, Xin Song, Xiang Zeng +4
Semi-supervised entity alignment (EA) is a practical and challenging task because of the lack of adequate labeled mappings as training data. Most works address this problem by gene…
Improving Knowledge Graph Entity Alignment with Graph Augmentation
Feng Xie, Xiang Zeng, Bin Zhou +1
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs…
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
Xuechen Zhao, Jiaying Zou, Zhong Zhang +3
Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-inv…
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
Feng Xie, Zhong Zhang, Xuechen Zhao +5
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g…
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Feng Xie, Zhong Zhang, Liang Li +2
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmi…
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting
Feng Xie, Zhong Zhang, Xuechen Zhao +2
The accurate forecasting of infectious epidemic diseases is the key to effective control of the epidemic situation in a region. Most existing methods ignore potential dynamic depen…