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Adaptive Question Answering System

N. Lakshmi Prasanna*, Abhinav A.**, Soma Dinesh B.***, Srikari Bharathi D.****, Iswarya D.*****

*-***** Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.

Periodicity:January - March'2025
DOI : https://doi.org/10.26634/jit.14.1.21933

Abstract

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.

Keywords

Information Retrieval, BM25, Text Processing, Python Backend, Frontend Development.

How to Cite this Article?

Prasanna, N. L., Abhinav, A., Dinesh, B. S., Bharathi, D. S., and Iswarya, D. (2025). Adaptive Question Answering System. i-manager’s Journal on Information Technology, 14(1), 26-33. https://doi.org/10.26634/jit.14.1.21933

References

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Adaptive Question Answering System

Abstract

Keywords

How to Cite this Article?

References

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