What is Named Entity Recognition (Entity Extraction) in Semantic Analysis?

Search engines and language models do not read text the way people do. Before they can answer a question, they need to work out which real-world things a piece of text is about. This lesson introduces named entity recognition, also called entity extraction or entity chunking, and places it within the wider field of semantic analysis. It explains why marketers benefit from understanding how machines process text, so they can analyse content at scale in a similar way.

What this lesson covers

• What named entity recognition is and what it does to unstructured text
• Common entity categories such as people, locations, organisations, events and dates
• Why NER is a supervised, prediction-based machine learning technique
• Where entity analysis sits in the semantic analysis phase of NLP, and why compiler design is a useful analogy
• How sentiment analysis and topic modelling relate to entity extraction
• What pre-trained APIs such as Google Cloud’s return beyond a list of entities

The core idea is simple. NER takes text written for human readers, from a sentence to a whole web page or PDF, and identifies the named entities a model has been trained to recognise. It then classifies them into categories. This adds structure and meaning to previously unstructured text, which makes it an important pre-processing step for many language tasks, including those that large language models perform before they respond. Because the models are trained on labelled examples, they make predictions about whether a mention matches a known entity, often using surrounding context and other entities in the sentence to decide. Custom models can also be trained for specialised domains that need their own categories.

The lesson then zooms out to semantic analysis, the phase of natural language processing concerned with meaning. Alongside entity extraction it includes sentiment analysis and topic modelling, and it checks whether the relationships between words, phrases and entities make logical sense. Pre-trained APIs are versatile here. Besides entities, they can return entity sentiment, knowledge graph metadata, mention counts and the different phrasings used for the same entity. These data points become useful inputs whenever you bring entity analysis into keyword research, content audits or competitor work.

This lesson opens the optional Introduction to Entities and Entity Extraction knowledge primer. It gives you the vocabulary and mental model that the practical lesson on the Google Natural Language API and the core course modules build on. It suits SEOs and content marketers who want a clear grounding in how entity extraction works before applying it.