Bibliographische Detailangaben
| Titel: |
ADAPTIVE QUESTION ANSWERING SYSTEM. |
| Autoren: |
LAKSHMI PRASANNA N., ABHINAV A., SOMA DINESH B., SRIKARI BHARATHI D., ISWARYA D. |
| Quelle: |
i-Manager's Journal on Information Technology; Mar2025, Vol. 14 Issue 1, p26-33, 8p |
| Schlagwörter: |
QUESTION answering systems, SYSTEMS design, INFORMATION retrieval, PYTHON programming language, PYTHONS |
| 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 transformerbased 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. [ABSTRACT FROM AUTHOR] |
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