Optimization of Natural Language Processing of Academic Chatbot Using BERT Algorithm

Abstract

Natural Language Processing (NLP) has been profoundly transformed by Artificial Intelligence (AI), particularly in developing academic chatbot systems. This study optimizes NLP for academic chatbots by implementing a domain-specific fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. The primary objective is to enhance the chatbot's comprehension of conversational context, its accuracy in delivering academic information, and its pertinence in responding to inquiries from students and parents. The methodology involved collecting a corpus of 6,000 academic queries, followed by text pre-processing and fine-tuning the BERT model, with performance evaluated against traditional TF-IDF-SVM and LSTM baselines using accuracy and macro F1-score. The results demonstrate that the fine-tuned BERT model (Model C) achieved a superior accuracy of 95.8% and a macro F1-score of 0.95, significantly outperforming the LSTM (84.2% accuracy, 0.82 F1-score) and TF-IDF-SVM (61.5% accuracy, 0.59 F1-score) models. Furthermore, the model exhibited remarkable robustness, maintaining 93.5% accuracy on a challenging subset of queries with spelling irregularities, informal language, and grammatical errors. These findings indicate that the application of a domain-optimized BERT architecture effectively handles diverse and imperfect linguistic patterns, bridging the gap between generic language models and the specialized needs of the academic domain. The novelty of this study lies in its domain-specific fine-tuning of BERT for academic chatbot intent recognition, providing a replicable framework that can enhance institutional information systems and improve the quality of student and parent engagement.