Abstract
The short text in the online learning community is an important source of data in learning analysis. Therefore, the quality of the short text has a significant impact on the study of learning analysis. Due to the large amount of text data in the learning community, manual detection and repair will cost too much. This paper proposes a text detection and repair framework based on an online learning community. It aims to automatically detect and repair various types of semantic errors and grammatical errors that exist in online learning community short texts. The framework utilizes existing text error detection and repair algorithms and integrates them effectively to form a comprehensive detection and repair algorithm. In this paper, the validity of the framework is verified through experiments on the constructed data set. The experimental results show that the framework has high accuracy in automatically detecting and repairing text errors.
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