Learning relation prototype from unlabeled texts for long-tail relation extraction

• Main Authors: CAO, Yixin, KUANG, Jun, GAO, Ming, ZHOU, Aoying, WEN, Yonggang, CHUA, Tat-Seng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Annotations; Data mining; Knowledge Graph; long-tail; Prototype Learning; Prototypes; Relation Extraction; Training; Training data; Transfer learning; Urban areas; Databases and Information Systems; Data Storage Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7319; https://ink.library.smu.edu.sg/context/sis_research/article/8322/viewcontent/2011.13574.pdf
• Institution: Singapore Management University

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remainin...