A Grammar Error Detection and Correction System Based on Interactive Genetic Algorithm
Lei Zhao · 2024
Grammatical error is an important problem in natural language processing, which can seriously affect the readability and comprehensibility of text. Traditional grammar error detection and correction systems based on rules or statistical models often require a large amount of manually labeled data and a priori knowledge, and are difficult to effectively deal with complex and diverse grammatical errors. In this paper, a syntax error detection and correction system based on interactive genetic algorithm is proposed. The method utilizes the global search capability of genetic algorithm, combined with the interactive feedback from human experts, to automatically learn the features of grammatical errors and generate correction rules. Through iterative optimization, the system can gradually improve the ability of detecting and correcting grammatical errors. The experimental results show that the detection and correction system designed in this paper has an accuracy rate of up to 100%, and both the accuracy rate and recall rate exceed the traditional rule-based and statistical modeling methods.