Named entity recognition is the task of finding and labelling specific things in text, such as people, companies, places, dates, amounts or product codes. It turns free text into fields a system can store and search.
Data labeling is adding correct answers, such as categories, tags, boxes or ratings, to raw examples. The labelled data is then used to train or evaluate a machine learning model.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
Natural language processing is the field of AI that lets software understand, analyse and generate human language, in written text or in speech. Chatbots, email triage, sentiment analysis of product reviews and speech-to-text transcription in contact centres are everyday uses.
A knowledge graph is a structured network of entities and the relationships between them, stored so that both software and AI models can query connected facts.
An NLP engineer builds software that understands, extracts from or generates human language, using classic NLP and large language models. They turn text and speech data into reliable product features.
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