How to do entity recognition from raw text

NER (Named Entity Recognition ) is a subtask of information extraction that classifies a raw natural text to different predefined labels like Person, Date, Location, Sentiment, Monetary values etc.

Standford Natural Language Processing Group has made a library using which we can classify a given text which is in English to any of these predefined categories like a person, date, money, location etc.

This is the link to Standford NER Process demo.

Using this tool, we can classify text.

Here is an example of the demo:

The Great depression was severe worldwide economic depression in the decade preceding World War II. The timing of Great depression varied across nations, but in most countries it started in about 1929 and lasted until the late 1930’s or early 1940’s. It was the longest, most widespread and deepest depression of the 20th century.


The tags that come out of it are:

  • Location
  • Time
  • Person
  • Organization
  • Money
  • Percent
  • Date


In this text, 1929, 1930’s, 1940’s and 20th century is identified as Date.

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