Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field of artificial intelligence (AI) and computer science focused on the interaction between humans and computers using natural language. NLP enables computers to understand, interpret, and generate human language, allowing for more natural communication between humans and machines.
NLP involves several tasks, including language translation, sentiment analysis, speech recognition, text classification, and chatbots. Common NLP algorithms and models include tokenization, part-of-speech tagging, named entity recognition (NER), and machine learning models like transformers and recurrent neural networks (RNNs).
Technologies such as Google Translate, Amazon Alexa, Siri, and customer support chatbots heavily rely on NLP for their functionality. Modern NLP advancements, particularly in deep learning, have enabled machines to understand context, nuances, and ambiguities in human language, making human-computer interactions more intuitive.
How CodeBranch applies Natural Language Processing (NLP) in real projects
The definition above gives you the concept — but knowing what Natural Language Processing (NLP) means is different from knowing when and how to apply it in a production system. At CodeBranch, we have spent 20+ years building custom software across healthcare, fintech, supply chain, proptech, audio, connected devices, and more. Every entry in this glossary reflects how our engineering, architecture, and QA teams actually use these concepts on client projects today.
Our work combines AI-powered agentic development, the Spec-Driven Development (SDD) framework, CI/CD pipelines with agent rules, and production-grade quality gates. Whether you are evaluating a technology for your product, trying to understand a vendor proposal, or simply learning, this glossary is written to give you practical, accurate context — not theoretical abstractions.
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