Sentence Restructuring with User-Controlled Difficulty using NLP
Chaitya Shah, Aesha Shah, Lokita Varma, Sarthak Bhan, Nilesh Madhukar Patil · 2023
While there exists a substantial body of research focused on the simplification of sentences, research looking for ways to increase sentence complexity is apparently lacking. Understanding the intricacies of complex sentence structure, our research focuses on exploring the principality of sentence restructuring using user-controlled difficulty to enhance language learning and comprehension. By leveraging the WordNet dataset, Google Ngram Viewer, Lesk algorithm and Sentence Transformers, we propose a novel approach that provides users with a personalized restructured sentence. Considering the needs for students willing to pursue higher education, professional, linguistic and cognitive development, a model which helps increase sentence complexity will be fruitful for the users. This will enable users to express themselves more precisely and also develop stronger writing skills for their academic success. The model provided can also benefit individuals with diverse linguistic backgrounds which can aid in cultural exchange and understanding. Thus, to contribute to a deeper understanding to sentence structure, and fostering Natural Language Processing advancements, our research paper paves the way for future innovations in personalized language learning models.