Entity is a term used in natural language processing. Simply, it means a general term, like location or a person.

When the text analysis model is developed, it can recognize different terms. For instance, if we take an article about new iPhone, a text analysis algorithm can understand and extract terms like iPhone (=appliance), Apple (=organization), Tim Cook (=person), dates and etc. Here is a list of typical entities that Bitskout can recognize:


Custom Entities

More specialized algorithms can recognized legal terms, medical items and etc.

Here is an example from Amazon Machine Learning of a custom entity:

As you can see the algorithm can extract a insurance policy ID from text which allows you to detect whether the policy ID is present in a message or text or not.

Today, it is quite straightforward to build custom entities algorithm and there are a lot of providers for that. From Bitskout point of view, we can integrate the custom entities via external provider or run it on our premises (subject to evaluation).

Did this answer your question?