Keywords
Deep Learning
Transformer Models
Machine Translation
Sentiment Analysis
Bias
Healthcare
How to Cite
Abstract
Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, driven by the intersection of AI and linguistics. This paper provides a comprehensive review of the latest AI techniques in NLP, focusing on their applications, challenges, and future directions. We explore key areas such as deep learning, transformer models, and semantic understanding, highlighting their impact on NLP tasks like machine translation, sentiment analysis, and question answering. Additionally, we discuss challenges such as bias and ethical considerations, along with the potential of NLP in emerging fields like healthcare and finance. This review aims to provide researchers and practitioners with a deep understanding of the current landscape of NLP advancements and inspire future innovations in this rapidly evolving field.
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