<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=3529442&amp;fmt=gif">
Skip to content
All posts

Introduction to Ayfie Entity Extraction API: Potential Use Cases

Entity extraction is a technique used to extract textual entities from a body of text. It is also often used as input data for other features such as query completion and "did you mean.." spell checking.  

With the Ayfie Entity Extractor API you can fast and easy find entities such as date, location, organisation, and named persons. Another term used to describe entity extraction is Named Entity Recognition (NER) and the potential of usage areas is quite high.  
Here are some of examples of use cases for entity extraction or NER:  

 - Sentiment analysis of social media text  

- Online customer service  

- Chatbots  

- Text classification  

- Language translation  

- Search query understanding  

As you can see, there are many exciting potential usage areas for entity extraction. We hope that this introduction has piqued your interest and that you will explore the feature further. Entity extraction is just one of the many features that our NLP API offers. So if you are looking for more, be sure to check out our other features as well.   


Entity Extraction API example

The picture above is a screenshot of the API response of our Entity Extraction API in the Developer Portal.

The technology and natural language processing models behind such an entity extractor API are many. In general, the task of entity extraction can be boiled down to a few simple steps:   

- Tokenization: The process of splitting a string of text into smaller pieces called tokens. This is usually done by splitting on whitespace and punctuation. For example, "I'm going to the store" would be split into ["I'm", "going", "to", "the", "store"].  

- Entity Detection: Once the text has been tokenized, the next step is to detect which tokens are entities. This is typically done using some sort of machine learning algorithm.   

- Entity Classification: After the entities have been detected, they need to be classified into types. For example, "I'm going to the store" would be classified as ["I'm": person, "going": verb, "to": preposition, "the": noun, "store": noun].   

Entity extraction is a powerful tool that can be used in many different ways. We hope that this introduction has given you a better understanding of what it is and how it can be used. For more information about our API services check out the Ayfie API Services page and the specific one regarding the Entity Extraction API