A comprehensive survey on relation extraction: Recent advances and new frontiers
Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …
content. RE serves as the foundation for many natural language processing (NLP) and …
A comprehensive survey on deep learning for relation extraction: Recent advances and new frontiers
Relation extraction (RE) involves identifying the relations between entities from unstructured
texts. RE serves as the foundation for many natural language processing (NLP) applications …
texts. RE serves as the foundation for many natural language processing (NLP) applications …
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach
The sparsity of labelled data is an obstacle to the development of Relation Extraction (RE)
models and the completion of databases in various biomedical areas. While being of high …
models and the completion of databases in various biomedical areas. While being of high …
Enhancing relation extraction using multi-task learning with SDP evidence
Relation extraction (RE) is a crucial subtask of information extraction, which involves
recognizing the relation between entity pairs in a sentence. Previous studies have …
recognizing the relation between entity pairs in a sentence. Previous studies have …
A prompt tuning method based on relation graphs for few-shot relation extraction
Z Zhang, Y Yang, B Chen - Neural Networks, 2025 - Elsevier
Prompt-tuning has recently proven effective in addressing few-shot tasks. However, task
resources remain severely limited in the specific domain of few-shot relation extraction …
resources remain severely limited in the specific domain of few-shot relation extraction …
Few-shot biomedical relation extraction using data augmentation and domain information
B Guo, D Zhao, X Dong, J Meng, H Lin - Neurocomputing, 2024 - Elsevier
Relation extraction (RE) plays a pivotal role in biomedical information extraction. However,
traditional approaches are often limited by high data annotation costs and extensive time …
traditional approaches are often limited by high data annotation costs and extensive time …
Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages
A Halike, A Wumaier, T Yibulayin - Applied Sciences, 2023 - mdpi.com
Although low-resource relation extraction is vital in knowledge construction and
characterization, more research is needed on the generalization of unknown relation types …
characterization, more research is needed on the generalization of unknown relation types …
[PDF][PDF] Rethinking the Role of Entity Type in Relation Classification
Relation Classification (RC)—the task of identifying the relation between a pair of target
entities—is a fundamental sub-task of information extraction. RC models built on top of entity …
entities—is a fundamental sub-task of information extraction. RC models built on top of entity …
Information Extraction in Low-Resource Scenarios: Survey and Perspective
Information Extraction (IE) seeks to derive structured information from unstructured texts,
often facing challenges in low-resource scenarios due to data scarcity and unseen classes …
often facing challenges in low-resource scenarios due to data scarcity and unseen classes …
MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction
Multimodal information extraction (MIE) is a challenging task which aims to extract the
structural information in free text coupled with the image for constructing the multimodal …
structural information in free text coupled with the image for constructing the multimodal …