Adaptive question answering system

This paper presents an Adaptive Question Answering System designed to enhance information retrieval from text-heavy documents. The system integrates Natural Language Processing (NLP) techniques such as tokenization, stop word removal, and lemmatization to preprocess extracted text. By leveraging BM25 for document ranking and transformer-based models like BERT and T5 for answer generation, the system ensures accurate and contextually relevant responses. The backend is implemented using Python (Flask or FastAPI); while the frontend utilizes JavaScript, frameworks like React or Vue.js. This architecture facilitates an efficient and user-friendly interface for document uploads and querying, making complex information more accessible.