A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition
Qi Li, Tingyu Xie, Peng Peng, Hongwei Wang, Gaoang Wang · 2023
Distant supervision reduces the reliance on human annotation in the named entity recognition tasks.The class-level imbalanced distant annotation is a realistic and unexplored problem, and the popular method of self-training can not handle class-level imbalanced learning.More importantly, self-training is dominated by the high-performance class in selecting candidates, and deteriorates the low-performance class with the bias of generated pseudo label.To address the class-level imbalance performance, we propose a class-rebalancing selftraining framework for improving the distantlysupervised named entity recognition.In candidate selection, a class-wise flexible threshold is designed to fully explore other classes besides the high-performance class.In label generation, injecting the distant label, a hybrid pseudo label is adopted to provide straight semantic information for the low-performance class.Experiments on five flat and two nested datasets show that our model achieves state-of-the-art results.We also conduct extensive research to analyze the effectiveness of the flexible threshold and the hybrid pseudo label.