Question Answering System on The SQuAD Dataset

Project Description & Abstract

Question Answering System is an intelligent web-based application designed to provide users with accurate and relevant answers to questions written in natural language. The system applies Natural Language Processing (NLP), Machine Learning, and text-processing techniques to understand the meaning of a user's question and retrieve the most appropriate answer from a predefined knowledge base or dataset. The application provides an interactive chat-based question-answering interface, allowing users to enter questions in a simple conversational format. Users can also select a relevant category before submitting a question, helping the system narrow down the available information and improve the relevance of the response. The system processes the user's question through multiple stages, including text preprocessing, tokenization, keyword extraction, question analysis, context identification, and answer retrieval. Based on the processed information, the system searches the available knowledge or dataset and returns the most relevant response to the user. The application can be developed for specific domains such as education, libraries, healthcare information, travel, customer support, technical assistance, and institutional knowledge management. Domain-specific datasets can be integrated to improve the accuracy and relevance of responses. A major advantage of the system is that users do not need to manually search through large documents or databases. Instead, they can ask questions using natural language and receive information through a simple conversational interface. This makes the system particularly useful for students, researchers, employees, customers, and general users who need quick access to information. The system can maintain question-and-answer interactions, allowing users to review previous conversations and improve the overall user experience. The architecture can also be extended with more advanced NLP or deep learning models to support better context understanding and more complex questions. The proposed system provides a foundation for building an intelligent AI-powered information retrieval and question-answering platform that reduces manual searching, improves accessibility to information, and provides a more natural way for users to interact with digital knowledge.