51 citations · 67 across the 6 of their papers we have counts for
6 papers
Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation
Ridong Han, Yawen Shen, Zhongnian Li +3
Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution s…
An Empirical Study on Information Extraction using Large Language Models
Ridong Han, Chaohao Yang, Tao Peng +4
Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (…
Determined Multi-Label Learning via Similarity-Based Prompt
Meng Wei, Zhongnian Li, Peng Ying +2
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each tra…
An Empirical Study on Information Extraction using Large Language Models
Ridong Han, Chaohao Yang, Tao Peng +4
Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (…
Document-level Relation Extraction with Relation Correlations
Ridong Han, Tao Peng, Benyou Wang +2
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual represent…
Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception
Ridong Han, Tao Peng, Jiayu Han +2
Wrong-labeling problem and long-tail relations severely affect the performance of distantly supervised relation extraction task. Many studies mitigate the effect of wrong-labeling…