Introducing NER
Explore how spaCy's Named Entity Recognition (NER) tagger detects and classifies real-world objects such as persons, locations, organizations, dates, and more. This lesson demonstrates NER tagging on example sentences and a real-world news article, helping you understand entity types and their usage in natural language processing.
We'll cover the following...
We opened this chapter with a tagger, and we'll see another very handy tagger—the NER tagger of spaCy. As NER's name suggests, we are interested in finding named entities.
Named entities
What is a named entity? A named entity is a real-world object that we can refer to by a proper name or a quantity of interest. It can be a person, a place (city, country, landmark, famous building), an organization, a company, a product, dates, times, percentages, monetary amounts, a drug, or a disease name. Some examples are Alicia Keys, Paris, France, Brandenburg Gate, WHO, Google, Porsche Cayenne, and so on.
A named entity always points to a specific object, and that object is distinguishable via the corresponding named entity. For instance, if we tag the sentence "Paris is the capital of France," we parse "Paris" and "France" as named entities, but not the word "capital." The reason is that "capital" does not point to a specific object; it's a general name for many objects.
NER categorization is a bit different from POS categorization. Here, the number ...