Named Entity Recognition (NER) is a particular task in Natural Language Processing (NLP). It involves detecting and extracting entities from a passage of text.
In Legal NLP, this is more commonly known as Extraction or Legal NER. The goal is for the model to automatically extract an entity name, phrase of text or even particular clauses.
Extraction is useful in going from unstructured data in the form of contracts to structured data in the form of a table of fields. This enables higher-level analytics on the data which can drive business insights. Extraction also tends to be used to reduce the need for human capital when carrying out document review.