Entity Types – Quick and easy documentation

  • 9 November 2021
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Sofia Canino
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Do you know what Named Entities are? Can you identify their types? Can you make a distinction between them?

 

If you answered “no” to just one of these questions, then this tutorial is right for you.

Whether you work or not in the NLP/NLU field, nowadays it’s important to know that we call “entities” the key information contained in any text, such as people, places, organizations, companies, dates, email addresses, and so on.

 

In this tutorial, we will:

- make a distinction between Proper Nouns and Heuristic or Semi-semantic Structures, which are the two forms through which entities can be conveyed

- identify each entity type, according to Expert.ai fine-grained categorization

- provide examples and useful tips to help you distinguish between the different entity types

 

Enjoy it!

 

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mariakatosvich
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Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.

In any text document, there are particular terms that represent specific entities that are more informative and have a unique context. These entities are known as named entities , which more specifically refer to terms that represent real-world objects like people, places, organizations, and so on, which are often denoted by proper names.

A naive approach could be to find these by looking at the noun phrases in text documents. Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.

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