What is entity extraction?
Entity extraction, also known as entity identification, entity chunking, and named entity recognition (NER), is the act of locating and classifying mentions of an entity in a piece of text. It is done using a system of predefined categories, which may include anything from people or organizations to temporal and monetary values.
The entity extraction process adds structure and semantic information to previously unstructured text. It allows machine-learning algorithms to identify mentions of certain entities within a text and even summarize large pieces of content. It can also be an essential pre-processing step for other natural language processing (NLP) tasks.
Why is entity extraction important?
Entity extraction is one of the building blocks of natural language understanding. Adding structure to text paves the way for a whole host of more complex NLP tasks and is an end goal in itself.
Through entity extraction, it’s possible to start summarizing texts, locate mentions of particular entities across multiple documents and identify the most important entities in a text. These have various business use cases, from improving search and product recommendations to automating customer support processes.