From chatbots to coaching to document processing, there are many use cases of named entity recognition (NER). And with the Natural Language (NLP) market estimated to be valued at $43 billion by 2025, there will be many more.

But before that, let’s look into exactly how NLP fuels the named entity recognition process and how NER works.

Named Entity Recognition, a Subset of NLP

NER is a subset of NLP. And NLP works based on AI. NLP is the technology that helps machines understand the way humans speak. 

It works by applying calculations to the specific features of words and phrases, such as word types and capitalizations. Based on that, the AI can identify sentiments and discern what the context of the text means.

In the case of NER, NLP and machine learning (ML) serve two different purposes. NLP studies the structure and the language’s rules and interprets the context. ML helps the machines (or algorithms) learn based on the data being fed and improves their ability to understand over time. Combining these two, you have a program to identify and categorize texts.

Types of NER Systems

Currently, four different named entity recognition systems or approaches are used to identify these entities.

Dictionary-Based Systems

In this case, the algorithm is trained based on a dictionary of select words or phrases. Here, the basic string-matching algorithms find relevant entities based on what’s present in the dictionary. 

This system has several limitations. The dictionary has to be updated repeatedly to cover everything you need. Additionally, these systems can’t detect misspelled words and phrases, which might lead to inaccuracies in the output.

Rule-based Systems

The algorithm relies on specific rules in rule-based systems to identify and extract necessary information. It’s based on two types of rules:

  • Pattern-based: Morphology of the word being used.
  • Context-based: Context of the word being used.

For example, if the rule says that the word is present after a title like Mr./ Ms./ Mrs./ Dr., the word is a person’s last name.

Machine Learning-based Systems

Here, statistical models are used to identify entities. First, the model is trained using annotated documents, after which it can identify specific entities in other documents. 

The time it takes to train the model depends on how complex the terms are, but other than that, it’s a much more practical approach. This approach is preferred as it can identify entities when a word or phrase is misspelled, giving a better output. 

Deep Learning-based Systems

Deep learning (DL) is a fairly new approach used for NER, but it’s a lot more powerful than other approaches. DL-based systems use several models to achieve the desired output. Some of them include Bidirectional Encoder Representations from Transformers (BERT, Bi-directional Long-Short Time Memory (BiLSTM), Convoluted Neural Network (CNN), Generative Pre-trained Transformer (GPT-2 & GPT-3), Pathways Language Model (PaLM), and XLNet. These models read the annotated text to understand its context and train accordingly.

It can understand the text with more depth and provide an accurate output. It also saves time in feature engineering, which is a huge bonus.

How Does NER Work?

Ideally, there are two steps in the named entity recognition extraction process: detecting and categorizing entities. 

Detection of Entities

You need to train the algorithm to identify the entity based on your chosen approach. Let’s say you want the algorithm to identify three parameters—name, organization, and location. It’ll have to identify these entities first.

The algorithm can identify where these entities begin and end using the Inside-outside-beginning tagging approach. It can determine the boundaries and pull the information accordingly. 

To ensure accuracy, the model needs to be trained with the right data.

Categorization of Entities

Once the models are trained, you need to test them on different documents to identify their accuracy. Here, it reads the text and assigns the category to specific words if it meets the criteria. These criteria are predefined and depending on the approach, the model can get better with time — and it can be as complex or simple as you’d like it to be.

For a more granular understanding, we need to look at what kinds of blocks a typical NER model would have and how that works. 

There are three different blocks that a NER model has, and they are:

  • Noun Phrase Identification: This block includes all the nouns and can be identified using dependency parsing and speech tagging.
  • Phrase Classification: Once the nouns are extracted, they are classified into different categories depending on what you need. Examples include name, location, dates, money, time, and organization.
  • Entity Disambiguation: If the algorithm misclassifies entities, you can add a validation layer to ensure accuracy. For this purpose, you can use public knowledge graphs like IBM Watson, Wikipedia, and so on.

For example:

Sentence: “Bill Gates is the co-founder of Microsoft, a technology corporation based in Washington, United States.”


(“person”: “Bill Gates”),

(“org”: “Microsoft”),

(“location”: “Washington”)


Person = Bill Gates

Organization = Microsoft

Location = Washington

Use Cases of NER in Business

NER is extremely useful in any context that requires extracting information from text, audio, or video documents. There’s a potential application of NER in all these cases, be it historical documents, medical transcription, or sales and marketing. Here are a few:

Real-time Agent Coaching

You can use NER-based applications to train call centers or customer service employees. They can use the recordings of real conversations and identify which conversations resulted in the most sales. 

Using these recordings, you can identify keywords or phrases that repeatedly appear in these calls and classify them under different categories (positive intent, product issue, churn risk, consideration, and so on). Then, you can use custom trackers to identify these entities and generate suggestions.

Coupling it with Sentiment Analysis — another ML-based approach, you can identify the caller’s intent and direct the conversation accordingly. For example, if the application identifies a keyword that indicates positive buyer intent or potential for churn, it can make recommendations on tackling that sales call.

Customer Support

Many customers leave feedback through chat conversations, review sites, or emails. Using NER, companies can identify feedback relevant to a specific department, support personnel, or product and route it to them.

This helps keep all the support requests and feedback in check and automates the process while increasing the quality of service.

Human Resources

The applicant tracking system (ATS) is a widespread human resource (HR) tool. But did you know that these tools identify and filter out resumes using named entity recognition? NER can be used to determine the mention of specific skill sets, degrees, or experience (designation) and pull relevant resumes. 

You can also train managers on specific interview processes and questions to ask by showing them previous call recordings. 

Compliance and Moderation

With the anonymity of the Internet, moderation is getting harder by the day in online communities and forums. With the onslaught of spam and bullying, using NER moderators can filter out problematic posts and remove them as needed.

All moderators have to do is use a NER-based application that is trained on specific trigger words and images to look out for, and the application can do the rest for them.

Search and Indexing

Usually, webinars and other events aren’t as accessible as they should be because of the lack of transcription facilities. 

Using named entity recognition, conferences and similar events can be transcribed and translated into different languages in real-time so that attendees from anywhere can access them. 

In this case, NER is used to identify topic clusters based on specific keywords to create a topic hierarchy — using a parent-child hierarchy. This helps in clipping relevant clips for different topics and distributing them. The clips of the questions asked during the webinar can be stored in a database for future reference.

You can also monitor entities like emoji reactions to understand which topics strike a chord with the audience — and plan future webinars accordingly. The transcripts can also be made available online by indexing them so that you can measure the success of the content over time.

Live Captions and Meeting Notes

Companies can integrate NER-based applications with their Zoom or any other video calling application to transcribe conversations in real-time. 

Even better, these call notes could be summarized and recorded in a shared library to be accessible throughout the organization. This fosters collaboration and makes finding reference points for specific tasks and projects easier.

Final Word

Named entity recognition can be a handy tool for any business that wants to automate speech and text recognition. It can identify the text specified, understand its context, and provide an output based on how you want it classified. Additionally, NER can save thousands of hours that are typically wasted by manually sifting through text and speech records. It also helps you make data-driven decisions in varying contexts, ensuring your processes are as efficient as possible. 

By harnessing the power of artificial intelligence and machine learning, NER is blazing the road to make communication easier and more accessible for enterprises and individuals.

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Surbhi Rathore

Surbhi is co-founder and CEO of, a technology that makes it simple to deploy contextual AI and analytics across voice, text and video communication, for any stage software. Symbl is now a Series A startup with $24M in venture financing and 70+ team members across the globe.