Natural Language Processing (NLP)
Natural Language Processing (NLP) is a core area of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a meaningful way. It bridges the gap between human communication and computer understanding, allowing machines to process text and speech as humans do.
At its foundation, NLP combines computational linguistics—the rule-based modeling of human language—with machine learning and deep learning techniques. These technologies allow computers to analyze large volumes of natural language data, identify patterns, and extract meaning, context, and sentiment. NLP can handle tasks ranging from simple keyword detection to complex language generation and reasoning.
NLP applications are widespread in modern technology. It powers voice assistants like Siri and Alexa, translation tools such as Google Translate, chatbots and customer service automation, search engines, spam filters, and sentiment analysis systems that gauge opinions from text or social media. Businesses rely on NLP for analyzing customer feedback, automating document processing, and enhancing human–computer interaction across digital platforms.
From a technical perspective, NLP involves multiple subfields and methods, including tokenization, part-of-speech tagging, named entity recognition (NER), syntactic parsing, semantic analysis, and language modeling. Modern NLP models such as BERT, GPT, and T5 have revolutionized the field by using transformer architectures capable of understanding nuanced context, tone, and intent in text.
The ongoing development of NLP reflects the growing ambition to create systems that can engage in truly natural, context-aware conversations. As NLP evolves, it also raises important questions about bias, fairness, and privacy in language models trained on vast human-generated datasets.