Enhancing Semantic Understanding in Natural Language Processing: a Comprehensive Study of Contextual Embeddings and their Impact on Textual Inference and Business Applications

Abstract

This thesis explores the role of advanced semantic analysis in Natural Language Processing (NLP) using contextual embeddings like BERT and GPT, with a focus on their application in business contexts. The study begins by highlighting the limitations of traditional NLP models, which struggle to understand the complexity and nuance of human language, leading to ineffective performance in tasks such as sentiment analysis, customer service automation, and recommendation systems. The study emphasizes that the adoption of advanced NLP models can lead to more accurate, efficient, and context-aware systems, providing substantial benefits to businesses in customer interaction, market analysis, and overall operational efficiency.