Named Entity Recognition API

Named Entity Recognition API

Named entity recognition is a task of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, time expressions, quantities, monetary values, percentages, etc.

API description

About the API:

This API will analyze the text you give to it and it will recognize all the entities named in that text.

 

What this API receives and what your API provides (input/output)?

 

This API will receive the text you want to analyze. Later it will deliver all the recognized entities in a structured JSON response. 

Get the named entity, its start and end index for you to locate it on the text, and its label (money, org, date, etc) 

 

What are the most common uses cases of this API?

Extract Structure from Unstructured Text Data

Using NER allows you to parse data from documents and increase the speed and scale of content collection by extracting data such as numbers from earnings reports and linking company names to other content databases. Companies that want to organize and analyze unstructured data can use NER to create a structured database. In the private markets and loans space, extracting data from large volumes of PDFs and websites is tedious, time-consuming, and prone to human error. Using NER to tag and classify relevant data to extract information can aid and accelerate the process of assessing profitability and credit risk. Media and news companies can also use NER to identify companies on their own websites to enrich with additional data.

Enable Smarter Search

Personalization is key and part of that personalization is enhancing a user’s search experience. NER can be used as an early step in developing efficient search algorithms. NER can be run on all documents to extract entities associated with the documents and be stored separately. This effectively tags each document, and the next time a user searches for a term, that search term would be matched with a smaller list of entities in each document, which leads to faster search execution.

 

Power Recommendations

Another part of personalization involves recommendations. Today, recommendations often drive how we discover new content, products, and ideas. NER can aid recommendation algorithms by extracting entities from one document and storing these entities in a relational database. Data science teams can then create tools to recommend other documents that have similar entities mentioned. This can power news, application, and workflow recommendations.

 

 

Are there any limitations to your plans?

Besides API call limitations per month: 

  • Rate Limit: 100 requests per second. 

API Documentation

Endpoints


Locate and classify named entities mentioned in unstructured text into pre-defined categories.



                                                                            
POST https://zylalabs.com/api/415/named+entity+recognition+api/321/named+entity+recognition
                                                                            
                                                                        
Named Entity Recognition - Endpoint Features
Object Description
Request Body [Required] Json

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Named Entity Recognition API REST API, simply include your bearer token in the Authorization header.

Headers

Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.


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  • 1,000,000 Requests / Month
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API Provider: SoftDev

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Category:

NLP

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