Exogenous and Endogenous Data Augmentation for Low-Resource Complex Named Entity Recognition - Lecture 1

Explore innovative data augmentation techniques for low-resource complex named entity recognition in this 14-minute conference talk presented at SIGIR 2024. Delve into the research conducted by Xinghua Zhang, Gaode Chen, Shiyao Cui, Jiawei Sheng, Tingwen Liu, and Hongbo Xu as they discuss both exogenous and endogenous approaches to enhance NLP models. Learn how these methods can improve performance in scenarios where labeled data is scarce, particularly for complex named entities. Gain insights into the challenges and potential solutions for advancing named entity recognition in resource-constrained environments.

SIGIR 2024 M2.5 [fp] Exogenous & Endogenous Data Augmentation for Low-Res Complex Named Entity Rec