Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables machines to understand, interpret, and generate human language. It bridges the gap between human communication and computer systems by allowing software to work with text and speech data in a structured way.
How NLP Works
NLP systems start by breaking language into parts - words, phrases, or sentences. They then apply rules or machine learning models to identify structure, meaning, intent, and sentiment. Common tasks include language detection, part-of-speech tagging, entity recognition, and syntactic parsing.
Modern NLP often uses deep learning, especially transformers. These models, like BERT and GPT, process language in context, helping machines understand nuance and ambiguity better than older approaches.
NLP is not just about understanding. It also enables systems to generate language. Chatbots, voice assistants, and translation tools rely on NLP to produce clear, human-like responses.
Where NLP Is Used
NLP powers everyday tools such as spell checkers, autocomplete, and voice commands. In enterprise use, it enables email filtering, helpdesk automation, sentiment analysis, document summarisation, and contract review. Companies use NLP to process customer feedback, monitor social media, and build regional language interfaces. It also supports multilingual chatbots that can serve users across states and languages.
What You Should Know
NLP systems work best when trained on relevant, high-quality data. Bias in training data can lead to biased output. Accuracy varies based on language complexity and domain. For regulated environments, explainability and audit trails remain critical.
