It’s not easy being a robot. For natural language processing (NLP) to work, robots must process, understand and reference the information that has been entered against a ridiculous amount of data and create an intelligible response in a matter of milliseconds.

With you here, it’s hard to assume you’ve never heard of NLP before. In fact, the industry uses different names to refer to natural language processing, with NLP being the most common abbreviation used for this purpose.

It is also known as conversational AI and talking robots. Regardless of what you call it, it is a machine learning application that trains computer programs to understand human speech.

According to Gartner, by 2022, up to 70% of people will interact with conversational AI platforms on a daily basis.

What is Natural Language Processing?

This is quite a comprehensive blog showcasing 37 NLP applications across 10 different industries, giving you a clear picture of how beneficial it is turning out to be. But before we venture into that discussion, it’s imperative to better understand this discipline of artificial intelligence (AI).

One of the simplest ways to describe it is:

Natural language processing (NLP) is the interpretation of human language by a machine.

Every language has grammatical and semantic rules. However, there are also exceptions. Even the same word in the same language can mean something completely different in different countries or contexts.

So, if it is difficult for someone who speaks English, for example, to interpret Spanish. The same holds true for a machine. And this is where machine learning comes into play as a tool to solve this problem.

Machine learning is the process of extracting knowledge from data. And relying on machine learning, it is possible to improve the performance of NLP.

So, natural language processing (NLP) relies on the prowess of machine learning to display the structure and meaning of texts. With NLP apps, organizations can successfully analyze text and extract information about different things, such as people, places, events, etc.

How NLP Works in AI and What Are Its Major Subcategories?

While NLP has generated quite a friction over the past few years, many people still fail to understand how natural language processing works from an AI perspective. We’ll try to ease you out on the subject here.

What is expressed in writing or orally generates substantial amounts of data and information? Humans can communicate and interpret this information through the tone, the structure of a sentence, the use of idioms and a specific language, and the choice of certain words, expressions, and punctuations.

And it is through natural language processing that machines can understand that complex set of data and information. So, how do they do that? It’s by decoding unstructured data. Contemplating not only the language as a succession of symbols but also the hierarchical structure of natural language, that is, sentences or phrases as coherent ideas.

At this point, it’s also essential to understand the concept of NLU, Natural Language Understanding, a subcategory of NLP, which enables machines to understand incoming audio or text. Its counterpart is natural language generation (NLG), which allows the computer to react with an answer.

In short, the NLP is not only responsible for finding keywords. It also decodes the meanings and intentions of the words. Technically, it breaks the incoming language into small parts that can be processed and analyzed through text and speech vectorization.

This vectorization allows the machine to transform language into something it understands and give it meaning. Then, the AI ​​provides algorithms to the machine to identify and process the norms of the language. It also uses semantic and syntactic analysis to understand grammatical rules and find the real meaning of communications.

In the case of AI Chatbots, the NLP and NLU use machine learning to increase their intelligence over time. The intelligence of a Chatbot is subject to the training of these models by specialists.

But the fact is that you cannot confine the wonders of NLP to chatbots only. There are plenty of other applications helping the world communicate with machines conveniently and efficiently.

General Applications of NLP

The number of applications of NLP continues to increase with every passing day. We’re just compiling the commonest of them here to give you a broader picture of how NLP contributes to elevating different industries over time.


It involves searching for the most appropriate way to express a phrase or word in a different language. Perhaps the best example of an NLP translation app is Google Translate, which has gradually improved over the years. But initially, its performance was abysmal since it used phrase-based machine translation (PBMT).

The PBMT looked for similar phrases between the different languages, which made Google Translate relatively inefficient at finding phrases with the same meaning between the languages.

Google currently uses Google Neural Machine Translation (GNMT), which uses Machine Learning with NLP to look for different patterns between languages.

Speech Recognition

It reflects a machine’s ability to interpret a language’s phrases or words. This type of NLP app can be found on mobile phones and even in smart homes. The device recognizes what the term means by simply saying “Call John” and automatically starts calling John. Alexa and Siri are some of the most widely used speech-recognition NLP apps.

Sentiment Analysis

It is the determination of the emotional tone reflected in a sentence. For example, if a text announces a fall in the shares on the stock market, an NLP sentiment analysis app will predict that it is possibly a negative text. On the other hand, if a text refers to a party and everyone is invited to attend, the machine shall assume it to be positive news.

Question and Answer Systems

It involves automatically answering questions through a program. You can easily find these NLP apps in social media chats, calls, or tools like Siri or IBM Watson.

Automatic Summary Generation

This kind of NLP app is designed to obtain its main ideas and produce a coherent summary of any text. For example, if a person has to read The Time Machine by H.G. Wells, an automatic summary generation app can extract the book’s main ideas without breaking a sweat.


These are programs that engage in conversations with humans. For example, when a person needs to buy something in a virtual store and has specific questions about a product, he will likely receive automatic answers generated by an intelligent machine, i.e., a chatbot.

Market Intelligence

Market intelligence apps automatically start looking for related ads based on what a person has searched on the internet. A good example would be when you search for a specific product, and ads related to that product automatically appear on social networks.

Automatic Text Classification

It deals with assigning a label to a text according to its content and semantics. For example, when you receive an email, you can tell if it is spam or not based on its content.

Automatic Grammar Check

This kind of NLP app specializes in recognizing the different grammatical or spelling errors of a text according to the context.

How NLP Applications Are Benefitting Different Industries

NLP technologies have evolved tremendously, especially over the past decade and have become an essential part of our daily lives. Almost all of you have used Google Translate, AutoCorrect or AutoComplete at least once. The spam filter in your email system also uses NLP to determine which emails should be stored in your inbox and which are spam.

These are just a few examples, and there are many other wonderful ways in which natural language processing is used today.

The following section contains a comprehensive compilation of the applications of NLP in different industries, giving you a fair idea of the power and impact it manifests in our lives.

1. NLP Applications in Finance

Though NLP in finance has a handful of applications, in the financial services industry, four of them are surely worth mentioning:

     i) Expediting the Utilization of Unstructured Data

Financial services often have to deal with a massive data flow. Research and analytics reports, company records, and quarterly profit reports are just some of the financial resources needed by traders, investment managers, financial analysts, and other professionals. Financial data is also stored in various formats, such as pdf, XML, HTML, web, resources, and so on.

This accumulation of unstructured data increases the problem of analysis, making it more time-consuming and tedious. As a result, the necessary financial data, which can provide in-depth insight into the construction of future strategies, may be unusable and very likely to affect decision-making.

Natural language processing (NLP) allows you to extract valuable information from areas that remain underused. You can enable NLP models to analyze unstructured data, content, and information to address issues or trends that may affect financial markets.

NLP helps alignment processes in banks around the world. Unstructured data labeling facilitates the search for large numbers of digital records and allows compliance agencies to assess whether standards and rules are being implemented.

     ii) NLP-based Text Analytics for Added Efficiency

Text analysis is the process of extracting structured qualitative data from unstructured text, which is becoming increasingly important in the financial industry.
Emotion analysis is one of the most common measures of text analysis. It is a technique for determining basic emotions and isolating basic financial entities to understand the context of the text.

Using NLP text analysis tools, you can transform unstructured data sources that investors regularly use into an improved format optimized explicitly for cost. This intelligent format can be helpful in efficient data analysis, enabling structured data reading and data visualization, which increases the productivity and accuracy of data-driven decision-making.

Text analysis is primarily used for risk management and alpha generation in the finance industry. Institutions such as Bank of America and JP Morgan Chase rely on this technology. Compliance and risk managers, data researchers, quantitative investors, and many others use it to search for financial documents and obtain the necessary information.

     iii) Apt Financial Document Analysis

With AI technology, users can integrate their document financing solution into current workflows without disrupting existing methods. NLP allows financial professionals to read and understand a large number of financial documents automatically.

Companies can train NLP models using their existing documentation resources. The NLP-run financial statement analyzer then surfs through hundreds of these documents to extract and aggregate the most relevant and sophisticated information.

In addition, NLP plays a vital role in creating a search engine for financial market development. Tons of documents are stored in the database of financial institutions. The NLP-supported search engine salvages the concepts, notions, and elements confined in these documents to acquire valuable investment data.

Banks that use this technology include Deutsche Bank, Axis Bank, etc. They use it to automate and facilitate the processing of massive volumes of documents to extract key concepts and data fields from it.

     iv) Content Enrichment

Financial companies can leverage content enrichment to improve investment management, risk management, and compliance. Intelligent content enrichment systems are suitable for supplementing and enriching enterprise-wide data with rich contextual information containing accurate and valuable knowledge.

Content intelligence is considered a novice technology that manages content strategies by providing data-driven content analysis and its impact on consumers. It offers direct and indirect financial benefits from content marketing initiatives, such as increased return on investment and decreased squandering.

These tools use NLP technology to evaluate the tone of your content, discover formulas, and make decisions based on data.

They also help learn what the content is about and how to target the right topic.

McKinsey, a management consulting firm, has a content enrichment and information model that verifies data content and monitors quality and consistency. It allows them to assist their clients with wealth, insurance, and asset management.

2. NLP Applications in Marketing

There’s no denying the fact that marketing remains the top beneficiary of natural language processing. Let’s see how:

     i) Brand Awareness and Market Research

Understanding customers’ emotions are key to developing a business strategy. NLP-based software can be used to analyze social media content, product reviews, and customer content to track data.

Sentiment analysis also referred to as social intelligence, is used to see the context of positive and negative reviews focused on a brand. Algorithms work by creating comment-based sentiment analysis models. Grades are designed using the most common words and looking for familiar negative and positive phrases.

Each piece of information is then assigned a value, usually a number, indicating that the emotion is positive, negative, or neutral. With such data in hand, marketers can make more informed decisions when developing strategies and forecasts for the demand for goods and services.

     ii) Competitive Analysis

Competitive analysis is usually performed when starting a business or entering a new market segment. The analysis can provide a better understanding of the market, who the competition will be, and who the potential buyers will be.

NLP-powered engines can significantly simplify and automate the competitive landscape scanning process.

Tools are available to monitor competition, usually by searching for industry articles on the internet and using the information to nurture NLP models, which record semantic relationships between companies.

     iii) Social Media Marketing

One of the critical advantages of NLP is its ability to process unstructured social media data. Marketing professionals can identify the major influencers in the growth areas. Similarly, marketers can decide what content will resonate with social media viewers.

The goal is to target certain influential people with the right content to create awareness and communicate. According to a WSJ publication, South Korean carmaker Kia used NLP algorithms built into IMB Watson to identify social influencers who could propagate their 2016 Super Bowl campaign.

With this approach to employing influential people to implement its strategy, Kia showed a 30% increase in engagement with its brand.

     iv) Chatbots

Customer service automation also provides opportunities to seek betterment using NLP. Chatbots built on NLP technologies allow you to steer people away from mundane tasks to more exciting areas. The e-commerce and customer service sectors have been using it with great success for many years.

NLP chatbots can solve many user problems, especially giving instant answers to simple questions. Chatbots can also increase conversions by making it easier for users to find out what they want to buy and by improving lead generation, all with the use of call formats.

Since the Facebook Messenger platform launch, many brands have invested heavily in chat experiments. It has led to a significant increase in people resorting to NLP chatbots to improve their business prospects.

     v) Voice Assistants

The US hosts more than 110 million voice assistants featuring popular voice assistants such as Siri, Alexa, Cortana, and Google Assistant, receiving millions of requests each month. Tens of millions of users have also purchased voice-activated systems such as Amazon Echo and Google Home.

In the voice assistant industry, NLP is used to translate speech into text, make semantic comparisons from knowledge bases, and translate responses after converting text to speech. By using smart speakers as a marketing channel, businesses can increase their marketing activity and engage their audience more effectively.

3. NLP Applications in HR

Human resource is another area rightfully exploiting the NLP’s potential to streamline its affairs. Major NLP applications in HR include:

     i) Resume Evaluation

NLP can be used in combination with ML (machine learning) ranking algorithms to review candidates’ resumes, obtain relevant keywords (education, skills, previous roles), and rank candidates based on their profile, which corresponds to a specific position in the organization. In addition, NLP can produce candidates’ CVs that fit certain positions, allowing recruiters to review CVs more quickly and focus on specific job requirements.

     ii) Recruiting Chatbots

Recruitment chatbots, also known as recruitment assistants, are used to automate communication between recruiters and candidates. Chatbots use NLP to screen candidates’ CVs efficiently, schedule interviews effortlessly, respond to candidates’ questions about the position correctly, create candidate profiles appropriately, and facilitate onboarding.

     iii) Interview Assessment

Many large companies have started using online job interview platforms to interview candidates, especially after the COVID-10 pandemic. These platforms allow candidates to upload videos, answer job questions, and upload files such as certificates or letters of reference.

NLP comes in particularly handy for interview platforms in analyzing a candidate’s emotions, reviewing uploaded documentation, verifying references, discovering specific keywords that may reflect positive or negative behavior during the interview, and transcribing and summarizing videos for archiving purposes.

     iv) Employee Sentiment Analysis

NLP can determine job satisfaction, motivation, areas of friction, difficulties, and racial and sexual prejudice among employees. It also serves excellent at analyzing feedback, public emails, employee comments on social media and work panels, and so on.

All this assists HR in better identifying areas of conflict, potential successful employees, training needs, keeping employees engaged, and optimizing work culture.

4. NLP Applications in Retail and eCommerce

In the retail and eCommerce arena, NLP-powered chatbots with artificial intelligence are almost everywhere and multiplying rapidly. But that’s just the tip of the iceberg. NLP packs far more potential in helping retailers go above and beyond in identifying and exploiting the user intent to augment their sales volume and revenue.

     i) Customer Services Chatbots

A 2019 survey found that 65% of customer service handlers believe that NLP chatbots can understand the context of users, and 52% said that chatbots could automate actions based on user feedback.

Responding to clients via chatbots allows businesses to answer frequently asked questions, schedule appointments, book tickets, follow and process orders, upsell, and onboard on their behalf.

     ii) In-store Bots

Many stores use NLP-based virtual assistants to guide customers through their shopping ventures. You can have them either as a mobile app to help customers navigate the store or as touch-screen assistance in the store, facilitating customers to communicate via text or voice.

The robots in the store act as shopping assistants, designing products for customers, helping customers find the product they want, and providing information on upcoming sales or promotions.

     iii) Market Intelligence

Marketing professionals can rely on web scraping to retrieve e-commerce data (e.g., blogs, social posts, news sites) and product data (reviews, ratings, comments). Taking it one step further, they can use it with NLP capabilities for customer sentiment analysis, market trend detection, and marketing strategy optimization.

     iv) Semantic Searches

Semantic search describes a searching method that you can use to find keywords, understand the context of the search, and suggest appropriate answers. Many retail and eCommerce sites rely on NLP-based semantic search engines to take advantage of long search strings (such as white pants for women, size 38), understand customer intent, and improve product visibility. Merchants claim that e-commerce sites with a semantic search box boast shopping cart abandonment as low as 2% compared to 40% on non-semantic search pages.

5. NLP Applications in Telecom Industry

The telecom industry is advancing at an unprecedented rate, and many believe NLP applications have significantly helped them leapfrog in the past decade, as elaborated in the following examples.

     i) Automated Infrastructure Documentation Generation

Maintaining clean and organized infrastructure documentation and keeping it updated are essential for telecom operators. In the case of any network failure, it is necessary to look through it in search of an issue. If the language is too complicated or the use of terminology is not consistent, finding relevant information is highly challenging, particularly in the case of extensive networks.

With NLP, companies can partially automate the generation of critical infrastructure documentation, standardizing the documents, creating templates, and automatic suggestions that help keep the writing as clear as possible.

The NLP engine can verify the usage of terminology and suggest expressions. As a result, maintaining clean and updated documentation becomes much more manageable. It allows the network support to fix recurring issues faster, streamlining information flow and taking care of network optimization.

     ii) Telecommunications Network Log Analysis

With the environments becoming increasingly distributed, managing logs and resource utilization becomes more challenging than ever for telco operators. And as with the business growth, the log volume has to increase, making its efficient management a top priority. Troubleshooting the logs is a time-consuming and labor-intensive process, and that, of course, means elevated costs.

NLP can streamline that process, making it much easier and faster to detect issues in real-time and solve them. Since the logs usually contain written text, the developers can use NLP to classify and group them. Such structuring facilitates extracting relevant information concerning the issues.

     iii) Configuration of Network Management System (NMS)

Using NLP, telecom companies can empower their non-technical employees while avoiding costly training at the same time. With the support of the machine trained to interpret the natural language requests, they can configure a network management system without technical skills, just via speech.

The exact course of that process may depend on the developers’ approach. However, an NLP engine can extract the information from the command and convert it into a structured network request (SNR) that later gets translated and validated by the deep learning network without getting into too much detail.

The process ends with the translated SNR being processed by the network management system via API. Result? Fast and human-friendly configuration, less frustration and errors.

6. NLP Applications in Cybersecurity

Some of the major NLP apps in cybersecurity include:

     i) Spam Detection

NLP models can classify text to detect spam-related words, phrases, and emotions in email, text, and social messaging applications. NLP spam detection models generally follow the following steps:

  • Data cleaning and pre-processing: for removing add-ons and stop words.
  • Tokenization: for shortening and labeling sentences and paragraphs.
  • PoS (Parts of speech) tagging: marking a word in a sentence or paragraph to the appropriate part of the speech tag based on context and definition.
    The processed data is transmitted to the classification algorithm (e.g. decision tree, KNN, random forest) to classify it as spam or non-spam email.
    (This interesting blog elaborates on NLP techniques in detail.)

     ii) Prevention of Data Exfiltration

Data exfiltration is a security breach in which unauthorized data is copied or transferred from one device to another. To filter the data, attackers use cybersecurity techniques, such as tunneling a domain name system (DNS) (i.e., DNS queries that reflect a request for information sent from the user’s computer) to a DNS server and phishing emails that provide hackers with personal information. NLP can be used to detect DNS requests, malicious language, and text anomalies to identify malware and prevent data filtering.

7. NLP Applications in Transportation

The transportation industry has undergone immense evolution in the past few years, and so has the use of NLP applications in it. Let’s look at some of the major NLP applications helping the transportation industry thrive.

     i) Chatbots-led Efficiency

Chatbots are simple machines that can follow a relatively structured conversation for a specific task. They are being used in many ways in the transportation industry. Logistics and supply chain owners delegate many detail-oriented and repetitive tasks to them.

For instance, chatbots are being used to collect information and relay it back and forth between a driver and a dispatcher. If a driver arrives at a warehouse and finds that a shipment isn’t ready, he can report this and be notified when it’s prepared. Logistics managers waiting for the delivery can be advised that the driver will be late.

The driver receives an updated loading and delivery schedule and is rerouted to avoid traffic congestion. All this is arranged by interacting with a chatbot. When a driver communicates with a chatbot by voice or text, the system uses inference models to “understand” how to answer shipping-related questions.

Whenever NLP is integrated into any transportation management system, it keeps track of and comprehends interactions. The system recognizes the behavior patterns of specific users and foresees what they require, which helps to save precious time.

AI-based systems and NLP technologies assist in working with significant volumes of unstructured data to extract critical information on dates, addresses, billing, and much more.

The parcel-delivery company, UPS Inc., has been using chatbots since 2016 to find locations, track packages, and get other information. Customers can interact with chatbots by using text or voice commands, enjoying an enhanced experience.

     ii) Automotive Digital Assistants for Improved Driving Experience

Some influential automotive companies are already using automotive digital assistants powered by conversational artificial intelligence.

BMW and Mercedes are using Nuance’s Dragon Drive AI, a hybrid of voice and natural language technologies. The voice interface uses speech recognition, natural language understanding (NLU), and text-to-speech technology. Dragon Drive AI enables drivers to access services and apps through voice commands.

For example, if you say, “drive me to the nearest pizza parlor in New York,” your vehicle will take you there. You can find parking, the nearest gas station, and much more.
Simple in-car functions like selecting an address or a radio station use speech technology embedded in the car, independent from a data connection. For quick and accurate dictation or the latest information on points of interest, the power of the cloud is engaged.

Embedded and cloud-based technology are combined for optimal results. For those who don’t like talking, handwriting recognition is also available nowadays. All-in-all, the whole experience is intuitive, flexible, and safe.

     iii) Sentiment Analysis for Improved Passenger Experience

Social sentiment can measure the tone of a message and assign a value or score to it based on several factors. Is a comment positive, neutral, or negative?

A spike in negative posts may provide an early warning of a service issue at a train station or bus station. The root cause of the problem can be uncovered and corrected.

Sentiment analysis enables airport companies to offer targeted services to specific users. For example, travelers in airports can not only be guided to the right gate in the airport but receive dining and shopping suggestions specific to their needs along the way.

Companies are even planning and designing airports by leveraging sentiment analysis to make better-informed decisions.

Social sentiment analysis can also help companies in the transportation industry to evaluate the health of their brand and understand how they stack up against the competition.

Understanding how a target market feels about a company and its services by analyzing overall sentiment can provide some valuable insights and improve customer experience.

8. NLP Applications in Healthcare

Healthcare is among the industries not only leveraging on the advances in NLP but various other disciplines of AI, i.e., computer vision in healthcare, predictive analytics in healthcare, and augmented reality in healthcare.

However, we’ll confine our focus of attention to the benefits of NLP apps in healthcare in our discussion here and see how NLP in healthcare is stepping up the game.

     i) Clinical Documentation

It helps clinicians get rid of arduous physical EHR (Electronic Health Records) systems and allows them to dedicate more time to their patients. Formulated data entry and speech-to-text dictation both have been a real blessing for clinicians around the world.

     ii) Speech Recognition

Over the years, NLP has advanced in the area of ​​speech recognition by allowing physicians to transcribe notes for user input of EHR data. Front-end voice recognition eliminates the task of doctors taking notes instead of focusing on a problem, while background technology works to detect and correct any transcription errors before returning them to human correction.

The market is almost saturated with voice recognition technologies, but many startups are disrupting this space with deep learning algorithms in mining applications and discovering wider possibilities.

     iii) Computer-Assisted Coding (CAC)

The CAC records data from procedures and treatments in a bid to grab each possible code for optimal claims processing. Despite being one of the most famed NLP uses in healthcare, its adoption rate hasn’t gone past 30%. It surely increased the coding speed but did not achieve more accuracy.

9. NLP Applications in Manufacturing

Ironically, many people fail to grasp the idea of applications of NLP in manufacturing while it benefits from the advances in natural language processing a great deal.

     i) Process Automation

The use of NLP technologies in the manufacturing process allows the automatic processing of information in natural language and the execution of repetitive tasks like paperwork and report analysis.

     ii) Inventory Management

Analyzing data about the stock, sales, and user reports of certain products is essential to assess the correct decisions for a company to optimize and maximize profits. By leveraging NLP technologies, the resulting benefits are:

  • The entire process becomes more comprehensive
  • It is more difficult to incur errors related to the analysis of sales
  • It is easier to analyze the manufactured products and discard those with low quality without affecting the supply chain and sales

     iii) Emotional Mapping

Sentiment analysis and emotion detection are one of the most exciting features of NLP. Early NLP systems allowed organizations to collect speech-to-text communication without accurately determining its full meaning. Today, NLP approaches can sort and understand the nuances and emotions in human voices and text, giving organizations unparalleled insight.

     iv) Operation Optimization

Furthermore, NLP technologies can be employed to trace the performance of equipment and improve the interaction with machines. This simplifies the operation of complex systems and can enable Human-Machine Interaction where the operator and the machine collaborate in order to optimize processes.

10. NLP Applications in Supply Chain Management

Natural language processing can be used in the supply chain and logistics in many ways.

     i) Identification of Areas That Could Potentially Affect the Global Supply Chain Operations

Natural language processing is combined with other AI tools for unstructured data queries that analyze and revise publicly available information. This information can be viewed in blog posts, videos, social media, news, or other formats.

NLP identifies and reports on specific areas and keywords that may influence the supply chain. These include issues with specific suppliers, major changes in the environment, purchasing verification, competition monitoring, supply chain management, ethical practices, changes in policies and processes, reputation management, and likely future trends.

     ii) Information Capturing in Multiple Languages

Language barriers are a major problem for global supply chains and compliance with logistics (e.g., guidelines for trucks and truck drivers). NLP helps to solve this problem by allowing local stakeholders to communicate in their mother tongue. NLP analyzes, categorizes, and translates this data in a way that is useful to everyone.

     iii) Adaptive Chatbots and Interviews to Input Supply Chain Data

Chatbots are a natural implementation of NLP. They respond to customer inquiries and provide a readable result combined with the right context to enable more intelligent information collection and supply chain decisions. Chatbots search for information from suppliers through customizable interviews and ask tailored questions to get the information they need for optimized supply chains.

     iv) Query Supply Chain Data Information Using Natural Language

Since supply chains generate a large amount of data, supply chain optimization requires that this data be retrieved correctly. NLP allows users to ask complex questions and guides them with data, providing an overview of the answers to these questions.

     v) Responding to Downstream Supply Chain Stakeholders with Customer Service Automation

Natural language processing greatly simplifies customer service automation. Stakeholders ask questions, and the NLP responds with the right information or takes them to the right area. This reduces administrative costs in customer support centers and improves customer satisfaction throughout the supply chain.

11. NLP Applications in Education

NLP has been deemed as one of the most innovative and dramatic ways to advance the world of learning and education now and in the years to come. The treatment of natural language and its use in teaching has already been successful. It is already being used to improve students’ reading and writing skills and has already yielded many positive results.

     i) NLP Applications for Improving Writing

NLP is currently used in the classroom to help children improve their writing skills. For example, if a student uploads an essay, NLP language learning technology can provide meaningful feedback to help them improve. This instruction applies to prose, grammar, and writing mechanics in general.

Grammatically, an NLP-based software that makes writing clear and error-free is an excellent manifestation of NLP’s applications. The software analyzes the text using AI technology and makes suggestions for changes.

     ii) NLP Applications for Improving Reading

If students are having trouble reading in class, there are NLP applications in class that can help. NLP algorithms can quickly detect errors in a student’s reading skills and provide automatic real-time feedback on how they can be improved.

Of course, a teacher would never have the time or resources in a classroom full of kids to walk around and give this kind of feedback to every student, which is why NLP solutions are becoming more and more popular.

Another great feature of NLP in the classroom is that it can help students adapt to the reading content that works best for them, both in terms of difficulty and productivity.

     iii) NLP Applications for Behavior Motivation

One of the most difficult tasks of a teacher is to encourage children to work and learn. A student doesn’t take long before getting tired and losing sight of the rest of the lesson. That’s why there’s so much emphasis on keeping kids engaged and motivated.

And that is why the transformation of natural language has become such a fascinating tool in education. NLP and machine learning technologies can assess the language spoken by teachers and students in the classroom and determine their state of mind during class. Teachers can use this research to see how students respond to the real-time teaching approach and what can be done to engage them.

The fact of the matter is that different aspects of the role of NLP in education have just begun to reveal, and we’re going to see much of it in the coming years.


We live in a world in which humans surely differ from other species for having efficiently developed tools such as language. We constantly communicate, speaking with words and gestures. We are surrounded by symbols, signs, indications, ones, and zeros. NLP comes forth as a fundamental tool that we must learn and master in order to train our machines and make them much more versatile when interacting with the environment, giving them the ability to better understand, explain themselves, and communicate.

If all this intrigues you to identify and capitalize on the potential of NLP applications to improve your business, share your concerns, ideas, and apprehensions with our NLP experts and see the magic happen right before your eyes.