How is Natural Language Processing Used in Business Today?

Have you ever taken your ability to understand and speak language for granted?

If so, don’t worry, you’re not alone. A power that becomes so automatic for us is easily under-estimated.

But when you stop and think about it, our ability to derive meaning from otherwise meaningless shapes and sounds is truely magical.

Our language is a part of our human intelligence that sets us apart from other species. For us Homo sapiens it’s been a huge advantage on an evolutionary and individual scale.

And now, businesses are gaining these advantages too. They are realising that combining language with technology gets them ahead of their competition. Natural selection at its finest.

This movement is being driven by the adoption of Natural Language Processing (NLP).

You may know this, but I want to be clear, NLP is a branch of AI that’s being applied by businesses to provide solutions to diverse range of problems. The global NLP market size is projected to grow from USD 20.98 billion in 2021 to USD 127.26 billion in 2028 at a CAGR of 29.4% over the 2021 to 2028 period. Huge sh*t right?

The goal of this article is to explain to you in detail, the following: 

  • What NLP is
  • How NLP works 
  • The functions NLP enables
  • NLP applications in business
  • Challenges of Natural Language Processing
  • The future of NLP

It won’t be a 2-minute reading, so, if you are keen to know about NLP and how it can be applied in business, grab a cup of coffee, sit back and explore the area with me! ☕️ 🤖 .

What is Natural Language Processing (NLP)?

NLP enables computer systems to capture, understand, and interpret texts and spoken words similar to how humans do. NLP makes use other technologies like computational linguistics, machine learning, and deep learning to enable systems to recognise, interpret, and even respond to texts and spoken words.

One of the major goals of the cutting-edge tech is to improve how we live and interact with our world. Organisations are leveraging NLP to upgrade their business processes and customer interactions. Some examples of NLP-enabled systems include voice-activated GPS programs, speech-to-text systems, chatbots, digital assistants, OCR receipt scanners, and trading bots.

How Does NLP Work?

NLP understands and interprets texts and spoken words by breaking down language into small units and translating them into formats that computer systems can interpret. This approach allows systems to analyze the pieces to understand how they come together to make meaning. NLP systems have programs that enable them to process data similar to how humans process inputs with their brains. Of course, NLP programs are much more simple and abstract away the complexities and unknown mechanisms in brains.

Two major processes underlie how NLP works:

  1. Data preprocessing 
  2. Algorithm development

Data preprocessing deals with getting text information ready for the analysis by machines. At the preprocessing stage, data is prepared and converted to a format the algorithm can work with. 

Some of the ways preprocessing can be done

  1. Word Segmentation: Involves breaking down texts into shorter pieces (AKA segmentation).
  2. Stop Word Removal: Involves “cleaning” up data to get rid of common elements, leaving important words that are necessary for interpretation.
  3. Lemmatization and Stemming: Words are broken down to their root forms.
  4. Part-of-Speech Tagging: As the name implies, words are classified based on part of speech.

Algorithm development comes into play after the data has been preprocessed. So the data is processed further – using a natural language processing algorithm.

Examples of NLP algorithms

  • Rules-Based Method
  • Statistical Method
  • Neural Networks

Rule-Based Method was developed and used during the early days of natural language processing – it is still in use. This approach creates NLP programs designed using symbolic methods – coding a set of rules (linguistic rules) manually, in combination with dictionary lookup. 

Statistical Method is based on machine learning algorithms. The systems learn from training data inputs and execute tasks based on the same. They review their methods as they work on more data inputs.

Neural Networks for machine learning has been embraced since 2015 while shifting from statistical methods due to its major downside of needing a great deal of feature engineering. Using word embeddings to get semantic features of words is a prominent neural networks technique.

Natural Language Processing Tasks

NLP has two major types of tasks: syntax analysis and semantic analysis. Syntax analysis is done to establish the meaning of a sentence based on grammatical roles. It involves breaking sentences into word groups and phrases for the purpose of forming a correct sentence. It covers the following: parsing, word segmentation, sentence breaking, morphological segmentation, and stemming.

Semantic analysis involves using NLP technology to analyze the meaning and structure of sentences. This type of analysis enables algorithms to understand sentences based on context, capture sarcasm & emotions, and extract meaning from a set of unstructured data. NLP systems use semantic analysis to interpret texts and spoken words like humans do. It covers the following: word sense disambiguation, named entity recognition, and natural language generation.

Functions NLP Enables

There are some key functions NLP enables. These capabilities are behind the real-world application of natural language processing. They include:

  • Text Classification
  • Text Extraction
  • Machine Translation
  • Natural Language Generation

Text Classification

As the name implies, text classification is the process of grouping words using tags. Text categorization is very important in NLP because it helps in extracting value from unstructured text. By using predefined tags or grouping words in a certain way, meanings behind texts can be captured and used accordingly. 

Categorizing words is useful in helping NLP algorithms capture the emotion or sentiment that underlies a text. In addition, text classification can help the algorithm to detect the intention of a writer or speaker – which makes predicting the action a speaker or writer may take with respect to the text they are generating.

Text Extraction

This is a process that involves the use of machine learning to extract keywords and phrases from a set of data that is unstructured. The unstructured data may be surveys, articles, and so on. These NLP functions can be used to shortlist entities, extract specific ideas, get keywords, get a summary of a document, and clean data.

Machine Translation

This involves the translation of text from one language to another by a computer without being aided by a human agent. For instance, translating a text written in German to Chinese without the intervention of a human operator.

Natural Language Generation

It is the process of producing content from a set of unstructured data using NLP algorithms. Natural language generation simply means transforming data into natural language. It involves analyzing a set of data and generating content based on the analysis. 

Based on this concept, there are systems that can mine large data sets, capture patterns, and generate reasonable content that is easy to understand for humans. This is useful in many fields, ranging from marketing, customer service to reporting. 

For instance, human reporters at Associated Press (AP) no longer review earning data after it is released to write a report on it. AP uses natural language generation to create reports automatically. This frees up reporters and allows them to focus on more productive tasks.

Natural Language Processing (NLP) Applications in Business

Here is the hot stuff you have been waiting for. How can we use NLP in business?

Thanks for asking… Natural language processing tools are very crucial for businesses nowadays and it can make a huge difference in today’s market. NLP can help organizations – across every industry – to improve businesses and find better & cost-effective ways to accomplish tasks.

Need some ideas? Here are some of the leading NLP applications in business:

#1 Social Media Monitoring

Social networking platforms play an important role in how organizations interact with customers and undertake their customer support responsibilities. Social media helps companies to develop relationships with consumers, enabling them to answer questions, get feedback, and derive useful insights. To maximize social media, companies use social media monitoring tools – developed with NLP technology – to keep tabs on what happens on various platforms. 

For example, you can monitor platforms for mentions of your brand and get notified when something is written or said about your business. NLP-enabled social media monitoring will help you respond to customers on time and prevent negative reviews from ruining the image of your brand.

#2 Chatbots & Virtual Assistants

Chatbots and virtual assistants are among the NLP technologies revolutionizing customer service. They are used to answer questions automatically. Using natural language processing, these programs are furnished with the ability to understand human questions and offer appropriate responses in real-time. Best of all, they learn from each interaction and become smarter over time.

Chatbots and virtual assistants are beneficial in many ways. Implementing them in your business will help accelerate response time and free up your customer support agents to focus on more demanding tasks. At the end of the day, your customers will be happy and you could cut down costs of operation.

#3 Speech Recognition

Speech recognition programs use NLP technology to transform spoken words into a format a computer system can read and interpret. This enables applications and devices to capture voice commands and respond as programmed. The use of speech recognition is seen in full display in virtual assistants, like Google Assistant, Siri, and Alexa. Google voice search and other voice-activated smart apps are examples of speech recognition technology that is transforming the business world.

Speech recognition can boost business productivity in a variety of ways. When it comes to multi-tasking and getting things done faster, speech recognition can significantly improve your businesses and help your employees get more done in less time. Converting your spoken words into text documents instead of typing makes work easier and faster – transcribe calls, send emails, translate, etc. In addition, speech recognition is used in the e-commerce industry to streamline in-app shopping. This can significantly improve the user experience and attract more customers.

#4 Automated Trading

AI-powered trading is more popular today than ever. NLP technology is applied in the finance industry, enabling traders to automate trades. This happens when you set up trading software to trade on your behalf. Trading tools are programmed to respond (place or exit a trade) when certain conditions in the market are met or not. Some of these parameters may include price, time, share price, etc.

Trading programs can also offer insights that help traders make investment decisions. NLP applications can review relevant reports, mergers, and acquisitions to provide recommendations on the most profitable investments. Based on such insights, investors can make profitable moves when considering stocks to buy.

#5 Optical Character Recognition

Natural language processing algorithms use optical character recognition (OCR) to analyze textual, image, and video data and to convert them into text-searchable files.

OCR receipt scanner APIs are used by organisations to beat fraud, automate accounting systems, gain insights into their customers, and more. NLP helps organisations to develop solutions and streamline business processes, boost productivity, and augment their competitive edge. 

Taggun uses OCR technology to process documents like receipts and invoices. OCR receipt scanner captures images and digitises the receipt into meaningful and structured data that other software can understand. You can integrate an OCR receipt scanner API with your system and process receipts & invoices automatically for your customers.

#6 Market Intelligence

Organisations use NLP algorithms to study their customers, target market, competitors, etc. This enables them to gather insights necessary for crafting winning marketing strategies. Given the fierce competition in every industry today, having access to timely market research data can help you stay ahead of the curve.

You can uncover ground-breaking business opportunities or enhance your competitive edge by analysing sentiment, subjects, and other parameters in unstructured data.

You can also leverage NLP tools to learn more about what your customers want and how to stand out from your competitors.

Downsides of Natural Language Processing

Even though NLP is highly beneficial in many ways, it is not devoid of challenges. The following are some of the downsides of natural language processing.


NLP systems can find it difficult to understand human speech when it is ambiguous. Due to the complexity of human speech, some other factors like slang, dialects, and social contexts can affect how machines capture, understand and respond to spoken words. Precision is one of the leading challenges of NLP, especially when it comes to understanding and interpreting human languages. Computers require communication with precise, unambiguous, and highly structured commands. Unfortunately, human speeches do not always come with those features.

Evolution of Language

Human languages change and evolve even though they have rules. As humans continue to advance, they create new words and find new ways to express themselves. This is a challenge in natural language processing because sophisticated algorithms that are useful now may become outdated as human language evolves with time.

Tone of Expression

Two persons can say the same thing but their meanings are not the same based on their tone of voice. Computer programs are not capable of capturing tone changes that alter the meaning of what is being said. This becomes a problem with speech recognition. NLP algorithms may not correctly interpret tone and inflection of speech – which also varies based on accents.

Low-Resource Languages

There are thousands of languages used across the globe. However, NLP programs are built mainly using some very popular languages. But some unpopular languages are underutilized. For the most part, there is not much data on most of these languages. The users of those languages are yet to see the power of NLP in full display.

Other challenges of natural language processing include errors in text and speech, irony & sarcasm, inadequate research and development, etc.

The Future of Natural Language Processing

Over many decades, the field of natural language processing has grown steadily. Artificial intelligence experts have had a lot of breakthroughs – that are responsible for the many applications of NLP in our world today. Several automation fields have natural language processing as their major driving force. That said, what does the future of NLP look like?

Natural language processing is expected to advance as researchers are relentlessly trying to find ways to overcome some limitations of NLP. In addition to solving NLP challenges, they are also working on incorporating the technology into other fields where they are absent at present. In a nutshell, the future looks amazing and bright for natural language processing. 

As research and development continue to advance, more data will be processed and NLP will be utilized in more helpful ways in new fields of study and business. Natural language processing is a very important part of the future. If you want your company to stand the test of time and be ahead of the curve, you have to consider a strategic adoption of NLP solutions.

To sum up 👇

Natural language processing is a branch of AI that offers solutions which are disrupting the business world, aiding market research, enhancing customer service, and helping organisations deliver goods & services efficiently.

According to a study, about 79% of executives believe that implementing AI solutions in business will make their work efficient and easier.

In another report, 75% of owners say AI in business will help them explore new opportunities.

The question now is: What can you do to enhance your competitive edge?

It is clear that adopting NLP solutions is the way to go.