Research progress on Chinese and English text error correction

International School, Beijing University of Posts and Telecommunications, Beijing, 100000, China

^* Corresponding author: wangyao1008@bupt.edu.cn

Abstract

Text error correction is an essential task in natural language processing (NLP) that focuses on automatically identifying and correcting errors in written text. With the increasing amount of digital text in both Chinese and English, errors such as typos, grammatical mistakes, and contextual ambiguities have become more prominent, affecting readability and communication. Over the years, various models and methodologies have been developed to tackle these challenges, evolving from traditional rule- based systems to more advanced statistical and machine learning-based approaches. This paper presents an overview of the current research status in Chinese and English text error corrections. By analyzing several papers involving Chinese and English error correction models, the article points out the types of errors such as spelling, grammatical and semantic errors included in text error correction and their basic elements. It discusses the advantages and limitations of the latest approaches including rule-based models, statistical and deep learning methods. The development potential of deep learning and big modeling techniques in the field of text error correction is shown. These findings help to advance automated error correction systems and their facilitation in the real world.