Instance-Based Error Detection for Part-of-Speech Tagging Dataset
Due to the lack of interpretability in deep learning frameworks,in this paper,we apply instance-based methods to error de-tection for part-of-speech tagging dataset for the first time aiming to leverage the similarity information learned between instances.Firstly,we implements an instance-based part-of-speech tagging model based on a pre-trained language model,achieving compara-ble prediction accuracy reaching 96.76％to models based on standard classifiers on the CTB7 dataset.Furthermore,we propose an instance-based annotation error detection method.To obtain an actual error detection dataset,several methods are employed to auto-matically detect errors in the CTB7 test set,and candidate errors are manually corrected,resulting in 2 016 annotation errors,ac-counting for approximately 2.5％of the total 80 000+words.Experimental results on the error detection dataset show that the error detection accuracy of the instance based method reaches 41.48％.
国家科技期刊平台
NSTL
万方数据
