<p class="jove_content">This study designs an adaptive learning system for extracting knowledge from text to answer questions, comparing Google-BERT, DistilBERT, RoBERTa, and TF-IDF model, using cosine similarity, on latency and semantic generalization. Google-BERT performs best, though the system remains sensitive to spelling errors, emphasizing the need for strong preprocessing for practical application.</p>

closed-domain-semantic-question-answering-system-as-use-case

Closed Domain Semantic Question Answering System as a Use Case of Transformer-Based BERT Models

To extract knowledge from text files and answer users' questions by finding the right information in context, an adaptive learning system, such as a ...

Institution - National Institute of Technology Arunachal Pradesh

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