An intelligent system for college admission queries: Leveraging BERT and Siamese BiLSTM for enhanced accuracy

College admission is a critical process that is pivotal in shaping a student's academic and professional future. College admissions queries include an extensive range of issues, from application standards and qualifying criteria to financial assistance and campus life. This research presents an intelligent system designed to address college admission queries with enhanced accuracy and efficiency. The system leverages the power of Bidirectional Encoder Representations from Transformers (BERT) and Siamese Bidirectional Long Short-Term Memory (Siamese BiLSTM) architecture to process and understand complex, context-dependent inquiries posed by prospective students. The data is sourced from multiple channels, such as historical admission records from the university's website and interaction logs from previous counseling sessions. The data was preprocessed using tokenization and lemmatization to avoid redundancy. Term Frequency-Inverse Document Frequency (TF-IDF) employed for feature extraction quantifies the query terms, allowing the system to identify significant words and improve query classification. a Siamese BiLSTM model was proposed for improved question classification and similarity matching. BERT generates contextual word embeddings that capture the semantic meaning of the words in the user's query. The system is capable of accurately classifying and understanding user queries, ensuring that responses are both contextually relevant and precise. Findings show that the proposed system achieves accuracy (94.7%), precision (93.6%), recall (93.8%), and F1-score (92.3%) while leveraging Python (version 3.x) for implementation. The results show that this integrated system outperforms traditional keyword-based query response systems, offering a more robust, scalable, and accurate solution for college admission-related queries.