Subject-Verb Agreement Error Handling using NLP: A Literature Review

Subject-Verb Agreement errors in English language are a common problem in Natural Language Processing. These errors occur when the Subject and Verb in a sentence do not agree in number or person, leading to a grammatically incorrect sentence. Over the past few decades, researchers have developed various approaches to automatically detect and correct these errors in text. In this paper, a comprehensive review has been provided of the existing literature on Subject-Verb Agreement error detection and correction in Natural Language Processing. This review study focuses on key techniques that are used in Rule-based, Machine learning, and Deep learning approaches to detect and correct the errors. The performance of the approaches is compared using various evaluation metrics, such as Accuracy, Precision, Recall, and F1 score on standard benchmark datasets. Additionally, the limitations and challenges of each approach are discussed, and potential directions for future research are suggested. This review will be helpful for researchers and practitioners in the field of Natural Language Processing, particularly those interested in Grammar error detection and correction in the English language.