Named Entity Recognition

Given a sentence or a piece of text, the objective of an NER model is to locate and classify text tokens as named entities in categories such as people's names, organizations and companies, physical locations, quantities, monetary quantities, times, dates, and even protein or DNA sequences. NER should tag the following sentence:

Ashish paid Uber $80 to go to the Twitter offices in San Francisco.

as follows:

[Ashish]_PER paid [Uber]_ORG [$80]_MONEY to go the [Twitter]_ORG offices in [San Francisco]_LOC.

Here is an example from the Google Cloud Natural Language API, with several additional classes:

Figure 3.1: An NER example from the Google Cloud Natural Language API

The most common tags are listed in the table below:

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Type	Example Tag	Example