山西大学学报（自然科学版）2024，Vol.47Issue(2)：251-259,9.DOI:10.13451/j.sxu.ns.2023166
基于实例的词性标注数据错误检测
Instance-Based Error Detection for Part-of-Speech Tagging Dataset
摘要
Abstract
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％.关键词
词性分类/标注错误数据集/语义相似度/CTB7数据集Key words
part-of-speech tagging/error detection dataset/semantic similarity/CTB7 dataset分类
信息技术与安全科学引用本文复制引用
崔秀莲,严福康,李正华..基于实例的词性标注数据错误检测[J].山西大学学报（自然科学版）,2024,47(2):251-259,9.基金项目
国家自然科学基金(62176173) （62176173）
江苏高校优势学科建设工程资助项目 （）
