BERT-QA: Empowering Intelligent Question Answering with NLP and Entity Recognition

Text analysis and comprehension are critical in various fields, enabling the need for utilizing advanced tools to complete these tasks. This study presents a comprehensive, feature-rich program designed to enhance the users’ capabilities in textual analysis based on real-time requirements. Key features of the package include multilingual translation to address the global nature of information exchange, and named entity recognition for identifying significant entities in documents, which is vital for knowledge management and information extraction. Additionally, the proposed approach enables sentiment analysis to evaluate different opinions and attitudes, crucial for social media monitoring and market analysis. An interactive Q&A module, powered by advanced BERT-based models, enables users to extract contextual insights efficiently, similar to searching the information within a textual source. By integrating these functionalities into a unified tool, the package aims to streamline textual analysis tasks, making them more efficient and effective, thereby empowering users to derive valuable insights from diverse text sources.