Design and Implementation of English Writing Aids Based on Natural Language Processing

Abstract

The design and implementation of English writing aids based on natural language processing (NLP) involve leveraging advanced algorithms and techniques to assist users in improving their writing skills. These aids can encompass various functionalities such as grammar and spell checking, style suggestions, vocabulary enhancement, and plagiarism detection. By analyzing the context, structure, and semantics of the text, NLP models can provide intelligent feedback and recommendations to help users express themselves more effectively. Additionally, interactive features such as real-time editing and personalized suggestions contribute to a seamless user experience. The paper presents a comprehensive exploration of the Sentimental Random Field Point (SRFP) approach in the domain of natural language processing (NLP). Through a series of experiments and analyses, we investigate the effectiveness and versatility of SRFP across various NLP tasks, including sentiment analysis, named entity recognition, text classification, and machine translation. Our findings demonstrate the robustness of SRFP-based algorithms and models in accurately classifying sentiments, enhancing grammatical correctness, expanding vocabulary, improving writing style, and achieving high performance in classification tasks. Through results it is observed that an accuracy of 92.7% for sentiment analysis using the BERT model, a precision of 85.3% and recall of 88.9% for vocabulary enhancement, and an accuracy of 93.5% for machine translation using the Transformer model.