68 citations · 164 across the 15 of their papers we have counts for
13 papers · 1 filter
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Honghao Gui, Lin Yuan, Hongbin Ye +4
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that…
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Honghao Gui, Shuofei Qiao, Jintian Zhang +6
Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that th…
Schema-adaptable Knowledge Graph Construction
Hongbin Ye, Honghao Gui, Xin Xu +3
Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such ap…
Generative Knowledge Graph Construction: A Review
Hongbin Ye, Ningyu Zhang, Hui Chen +1
Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be a…
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…
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…
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Shumin Deng, Jiacheng Yang, Hongbin Ye +6
Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leve…
Learning to Ask for Data-Efficient Event Argument Extraction
Hongbin Ye, Ningyu Zhang, Zhen Bi +5
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and e…
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi +20
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical langua…
Disentangled Contrastive Learning for Learning Robust Textual Representations
Xiang Chen, Xin Xie, Zhen Bi +4
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-o…
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can gras…
Contrastive Triple Extraction with Generative Transformer
Hongbin Ye, Ningyu Zhang, Shumin Deng +4
Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple e…
On Robustness and Bias Analysis of BERT-based Relation Extraction
Luoqiu Li, Xiang Chen, Hongbin Ye +4
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly…