Named Entity Recognition (NER)

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Named Entity Recognition

Identify key elements like people, places, and organizations in your text with our named entity recognition. It's easy to use and accurate, so you can focus on what's important.
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Benefits of Conversational AI
Extract entities from multiple sources

Identify named entities in a variety of text formats, including news articles, social media posts, and emails.

Data Classification

Classify named entities into different categories, such as person, place, organization, or thing.

Accurate and Scalable

Named entities that are ambiguous or that have multiple meanings can also be extracted accurately. Its agile and flexible nature can handle large amounts of data and can be scaled easily as per the requirement.

Use-cases of Named Entity Recognition


NER extracts information from financial statements, news articles, regulatory filings, and social media posts. It can be used to identify companies, risks, and fraud. This information can be used to make better investment decisions, assess risk, and prevent fraud.




Named entity recognition (NER) extracts information from electronic health records (EHRs), claims data, clinical notes, and patient surveys. Thereby helping healthcare organizations identify patients, predict outcomes, monitor compliance, detect fraud, and improve documentation. NER improves the quality of care by providing accurate information.


Data from aviation databases, news, social media, government reports,
conferences, and trade shows are extracted accurately by the NER. Later those data can be
used to identify aircraft parts, analyze maintenance logs, monitor flight data, automate tasks, and provide real-time information.



NER extracts data from a variety of sources like text documents- reports,
and emails; and from structured data like product data sheets, logs, sensor data, etc. It can
be used for product tracking, quality control, supply chain management, predictive
maintenance, and safety.


NER can be a valuable tool for lawyers, legal professionals, and businesses that need to
extract data from legal documents. It can simplify legal tasks such as contract review, legal
research, case law analysis, regulatory compliance, and fraud detection.



NER can be used to extract a variety of data from customer surveys, sales data, and previous
marketing reports, which can be used to improve marketing campaigns, target advertising, and
develop new products and service



Human Resource

NER extracts data from job postings, employee records, applicant tracking systems, and other sources. It can be used to identify and extract key information from resumes, categorize employees, identify potential candidates, track employee performance, automate HR
tasks, and generate reports.


Improved customer service:

Can be used to identify customer names and addresses, which can help businesses provide more personalized and efficient customer service.

Reduced fraud:

NER can be used to identify fraudulent transactions, which can help businesses save money and protect their customers.

Increased sales:

Potential customers can be identified quickly, which can help businesses increase their sales and revenue.

Improved compliance:

Can be used to identify and track compliance with regulations, which can help businesses avoid fines and penalties.

Reduced costs:

NER can be used to automate tasks, which can help businesses save money on labor costs.

Improved decision-making:

Quickly identify trends and patterns in data, which can help businesses make better decisions.

Increased innovation:

Can be used to generate new ideas and insights, which can help businesses innovate and stay ahead of the competition.

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