A comprehensive survey on relation extraction: Recent advances and new frontiers

X Zhao, Y Deng, M Yang, L Wang, R Zhang… - ACM Computing …, 2024 - dl.acm.org
Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Generative knowledge graph construction: A review

H Ye, N Zhang, H Chen, H Chen - arxiv preprint arxiv:2210.12714, 2022 - arxiv.org
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 …

Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction

X Chen, N Zhang, X **e, S Deng, Y Yao, C Tan… - Proceedings of the …, 2022 - dl.acm.org
Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

Differentiable prompt makes pre-trained language models better few-shot learners

N Zhang, L Li, X Chen, S Deng, Z Bi, C Tan… - arxiv preprint arxiv …, 2021 - arxiv.org
Large-scale pre-trained language models have contributed significantly to natural language
processing by demonstrating remarkable abilities as few-shot learners. However, their …

Document-level relation extraction as semantic segmentation

N Zhang, X Chen, X **e, S Deng, C Tan… - arxiv preprint arxiv …, 2021 - arxiv.org
Document-level relation extraction aims to extract relations among multiple entity pairs from
a document. Previously proposed graph-based or transformer-based models utilize the …

Ontoprotein: Protein pretraining with gene ontology embedding

N Zhang, Z Bi, X Liang, S Cheng, H Hong… - arxiv preprint arxiv …, 2022 - arxiv.org
Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …

Ontology-enhanced Prompt-tuning for Few-shot Learning

H Ye, N Zhang, S Deng, X Chen, H Chen… - Proceedings of the …, 2022 - dl.acm.org
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 …

The joint method of triple attention and novel loss function for entity relation extraction in small data-driven computational social systems

H Gao, J Huang, Y Tao, W Hussain… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the development of the social Internet of Things (IoT) and multimedia communications,
our daily lives in computational social systems have become more convenient; for example …

Defending pre-trained language models as few-shot learners against backdoor attacks

Z **, T Du, C Li, R Pang, S Ji, J Chen… - Advances in Neural …, 2024 - proceedings.neurips.cc
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-
shot learners. However, their security risks under such settings are largely unexplored. In …

Deepke: A deep learning based knowledge extraction toolkit for knowledge base population

N Zhang, X Xu, L Tao, H Yu, H Ye, S Qiao, X **e… - arxiv preprint arxiv …, 2022 - arxiv.org
We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …