Natural Language Processing (NLP): Definition + Examples

Natural Language Processing (NLP) has advanced from rule-based systems in the 1950s to sophisticated deep learning models today, enabling machines to comprehend and generate human language. This evolution has significantly impacted various sectors, including business, where NLP can enhance productivity by over 25% and contribute substantially to global economic growth. NLP is integral to applications like sentiment analysis, language translation, and virtual assistants, and its subsets, Natural Language Understanding (NLU) and Natural Language Generation (NLG), focus on understanding and generating human language respectively. Time-sensitive NLP processes data in real-time, beneficial for monitoring social media and news feeds. Businesses benefit from NLP through improved customer service, automated document processing, and fraud detection, leading to higher customer satisfaction and operational efficiency. Large Language Models (LLMs) offer advanced capabilities within NLP, providing valuable insights and automated solutions. Tools like Voiceflow enable businesses to implement NLP-driven chatbots easily, enhancing customer engagement and reducing costs.