About the API:
This API will analyze the text you give to it and it will recognize all the entities named in that text.
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)
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.
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.
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.
Besides API call limitations per month:
Locate and classify named entities mentioned in unstructured text into pre-defined categories.
Named Entity Recognition - Endpoint Features
Object | Description |
---|---|
Request Body |
[Required] Json |
{"app_version":"1.1","time_taken":0.03344082832336426,"msg":"NER successful.","ok":true,"ner":[{"entity":"AT&T","start_idx":0,"end_idx":4,"label":"ORG"},{"entity":"multi-billion-pound","start_idx":37,"end_idx":56,"label":"MONEY"},{"entity":"Vantage Towers","start_idx":70,"end_idx":84,"label":"ORG"},{"entity":"the weekend","start_idx":211,"end_idx":222,"label":"DATE"},{"entity":"Xavier Niel","start_idx":265,"end_idx":276,"label":"PERSON"},{"entity":"recent months","start_idx":415,"end_idx":428,"label":"DATE"},{"entity":"Vodafone","start_idx":429,"end_idx":437,"label":"ORG"},{"entity":"European","start_idx":580,"end_idx":588,"label":"NORP"},{"entity":"Niel’s Atlas Investissement","start_idx":687,"end_idx":714,"label":"ORG"},{"entity":"2.5%","start_idx":732,"end_idx":736,"label":"PERCENT"},{"entity":"Vodafone","start_idx":740,"end_idx":748,"label":"ORG"},{"entity":"last week","start_idx":749,"end_idx":758,"label":"DATE"}]}
curl --location --request POST 'https://zylalabs.com/api/415/named+entity+recognition+api/321/named+entity+recognition' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"language": "english",
"text": "AT&T is reportedly looking to sell a multi-billion-pound stake in its Vantage Towers business, a move that would help it reduce debt and keep shareholders off its back. The report, published by This is Money at the weekend, comes hot on the heels of the arrival of Xavier Niel amongst the telco’s investors and related talk on the speeding up of its streamlining process and movement on in-market consolidation. In recent months Vodafone has been fairly open about its intention to pursue its various options for its towers business, as well as examining M&A opportunities in its European markets, but there is a growing sense that it is not moving fast enough for certain shareholders. Niel’s Atlas Investissement, which picked up 2.5% of Vodafone last week, highlighted “opportunities to accelerate” in both areas, therefore it is likely no coincidence that the papers are starting to speculate on deals."
}'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long term commitments. One click upgrade/downgrade or cancellation. No questions asked.
The endpoint returns a structured JSON response containing recognized named entities from the input text. Each entity includes its text, start and end indices for locating it in the original text, and a label indicating its category (e.g., PERSON, ORG, MONEY).
The key fields in the response include "entity" (the recognized name), "start_idx" (the starting index in the text), "end_idx" (the ending index), "label" (the category of the entity), and metadata like "app_version" and "time_taken".
The response data is organized in a JSON format. It contains a main object with metadata fields and an array of entities, where each entity is an object with its respective fields (entity name, indices, and label).
The endpoint identifies various named entities, including persons, organizations, locations, dates, monetary values, percentages, and quantities, allowing for comprehensive data extraction from unstructured text.
Users can customize their requests by providing different unstructured text inputs to the endpoint. The API processes the text and returns entities based on the content provided, allowing for tailored data extraction.
Typical use cases include extracting structured data from documents, enhancing search algorithms, powering recommendation systems, and analyzing unstructured data for insights in sectors like finance, media, and research.
Data accuracy is maintained through advanced natural language processing algorithms that continuously learn from diverse datasets. Regular updates and quality checks ensure the model adapts to various contexts and improves recognition over time.
If the API returns partial or empty results, users should verify the input text for clarity and completeness. Adjusting the text or providing more context can improve entity recognition and yield more comprehensive results.
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