Applications of Hybrid Algorithms in Natural Language Processing and Understanding
R Raj Mohan, M Selva Kumar · 2024
Hybrid algorithms have emerged as a transformative approach in the realm of Natural Language Processing (NLP), integrating the strengths of rule-based systems and machine learning techniques to address complex language tasks. This chapter explores the theoretical foundations, applications, and recent innovations in hybrid algorithms, emphasizing their role in enhancing information retrieval, sentiment analysis, and language modeling. By leveraging the precision of rule-based methods alongside the adaptability of machine learning, hybrid approaches offer improved accuracy, contextual understanding, and user satisfaction. The chapter discusses performance evaluation metrics critical for assessing the effectiveness of hybrid models, highlighting the importance of metrics such as precision, recall, and user-centric evaluations. This comprehensive exploration of hybrid algorithms positions them as vital tools in the development of advanced NLP solutions, ultimately paving the way for more robust, efficient, and context-aware language processing systems.