Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a manner that is valuable. It involves a series of computational techniques that aim to interpret, generate, and understand human language in a way that is both meaningful and useful for computer applications134. This technology enables a range of applications, from automated chatbots that provide customer service to complex systems that analyze large volumes of text to extract meaningful insights3.

NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. These technologies enable computers to process human language in the form of text or voice data and understand its full meaning, complete with the speaker or writer's intent and sentiment. Through NLP, machines can perform a variety of language-based tasks at large scale, such as translating text from one language to another, responding to spoken commands, and summarizing large volumes of text rapidly—even in real time134.

Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are interconnected but distinct components of AI language technologies. Here's how they differ:

• NLP: Acts as an umbrella term encompassing both NLU and NLG. It focuses on the overall processing and analysis of human language, including tasks like tokenization, parsing, and sentiment analysis12.

• NLU: A subset of NLP that specifically deals with comprehending the meaning, context, and intent behind human language input. It involves tasks such as semantic analysis, named entity recognition, and intent classification13.

• NLG: Concentrates on generating human-like language output based on structured data or machine-readable information. It involves processes like content planning, sentence structuring, and text realization14.

While NLP provides the overarching framework for language processing, NLU focuses on interpreting human language input, and NLG specializes in producing coherent and contextually appropriate language output. Together, these technologies enable AI systems to engage in more natural and effective communication with humans, powering applications like chatbots, virtual assistants, and automated content generation tools25.

Natural Language Processing (NLP) faces several key challenges that impact its effectiveness and accuracy:

• Ambiguity and Polysemy: Words often have multiple meanings depending on context, making it difficult for NLP systems to accurately interpret text12. For example, "bank" can refer to a financial institution or a river's edge, requiring sophisticated contextual analysis to determine the correct meaning.

• Context Understanding: NLP models struggle with capturing the nuances of human language, especially in tasks requiring inferencing or commonsense reasoning1. This limitation affects their ability to interpret sarcasm, idioms, and cultural references accurately.

• Bias in Language Models: NLP systems can inadvertently perpetuate societal biases present in their training data34. Addressing this requires diverse datasets, multiple perspectives during training, and ongoing audits to identify and mitigate biases, ensuring more equitable and effective use of NLP technologies.