370 citations · 743 across the 23 of their papers we have counts for
4 papers · 2 filters
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Xiang Chen, Lei Li, Ningyu Zhang +4
Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relati…
Ontology-enhanced Prompt-tuning for Few-shot Learning
Hongbin Ye, Ningyu Zhang, Shumin Deng +5
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to ben…
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Peng Wang, Xin Xie, Xiaohan Wang +1
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to rea…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…