Application of Text Mining using Convolutional Neural Network for English Grammar Correction - Shastri, Shankarayya; Anusha, Anusha; K, Nisha; RN, Shilpa (International Journal of Computer Sciences and Engineering, 2025)
The application of text mining in natural language processing (NLP) has gained significant attention in recent years, particularly for tasks such as grammar correction, syntactic parsing, and error detection. One of the promising approaches for addressing these tasks is the use of Convolutional Neural Networks (CNNs), which, although originally designed for image recognition, have proven highly effective in extracting hierarchical patterns from sequential data, including text. This paper explores the application of CNNs for English grammar correction, leveraging their ability to identify local dependencies and complex grammatical structures within sentences. The approach involves training CNN models on large corpora of annotated text to automatically detect and correct grammatical errors, such as subject-verb agreement issues, tense inconsistencies, and word order mistakes. By convolving over word sequences, CNNs are capable of recognizing syntactic relationships and learning contextual cues that help in distinguishing grammatically correct forms from errors. The paper also discusses the benefits of CNN-based grammar correction, including improved accuracy, scalability, and the ability to adapt to diverse linguistic contexts. Experimental results demonstrate the effectiveness of this method compared to traditional grammar correction techniques, highlighting its potential for enhancing automated writing assistance tools, language learning applications, and real-time text editing systems. Ultimately, the integration of CNNs in text mining for grammar correction represents a promising avenue for advancing automated language processing systems and improving the efficiency of text-based communication.