Authors - Sunish Vengathattil, Shamnad Mohamed Shaffi Abstract - Citation resolution is essential for maintaining the accuracy and integrity of academic research. However, inconsistencies in citation formats, author name variations, and bibliographic errors make this process challenging. Traditional manual and rule-based methods are time-consuming and prone to errors, highlighting the need for advanced solutions. This study evaluates various machine learning models, including BERT, LSTM, and Random Forest, to improve citation resolution. Using AWS services for data storage, processing, and model training, the models were assessed based on accuracy and efficiency. Results show that deep learning models, particularly BERT, perform best in handling citation inconsistencies, though computational costs remain a concern. The findings emphasize the potential of machine learning in improving citation management for digital libraries and reference tools while suggesting future research for scalability and multilingual support.
