Natural Language Processing


AI Natural Language Processing: What is It and What Makes it Work? 

Speech is the most natural form of communication, and these days, technologies understand human speech. It’s all thanks to AI natural language processing, which has opened the door to countless use cases and increased productivity in personal and professional endeavors. 

We’re going to define what natural language processing in artificial intelligence means and see how AI NLP can be applied. 

Natural Language Processing

Speaker with voice activation on bookshelf /

What is AI Natural Language Processing? 

Within the realm of computer science, natural language processing (NLP) is a type of artificial intelligence that works to understand and process human speech. 

Computers work with structured data, but speech is naturally unstructured and is highly dependent on context. So, natural language processing works alongside natural language understanding (NLU) to comprehend and understand meaning. 

Key Concepts and Terminology Used in NLP 

When asking, “How does natural language processing work?” it’s important to understand a few key concepts that are involved. These include:

Sentiment Analysis

When people share their thoughts, they are linked to certain sentiments, whether it’s positive, negative, or neutral. Sentiment analysis derives the feeling behind the statements and is often used to understand customer feedback. 


Pragmatics is ultimately comprehension. It seeks to understand the language’s meaning, with context in mind to determine the use of language. In NLP, pragmatics is how the computer system understands the use of signs, words, and sentences within actual scenarios. 


Although semantics sounds like pragmatics, it is about questioning meaning. Pragmatics works in relation to the speaker and receiver whereas semantics is focused on meaning within the context. Within NLP, it is vital for accuracy as it ensures that the computer is understanding the keywords correctly. 


Tokenization breaks down characters, words, and subwords into tokens to be analyzed within a program. Many NLP tasks entail tokenization, such as: vocabulary building and word modeling. 

Keyword Extraction 

Within a stream of text, keyword extraction is applied to locate the most important phrases or words. For example, keyword extraction is part of search engine optimization (SEO) and business intelligence use cases. 

What is the Difference Between NLP and AI?

AI is artificial intelligence, which can be defined as, “the technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.” When you apply artificial intelligence to linguistics, that’s where NLP comes in! 

Many artificial intelligence techniques are used to make natural language processing effective, such as:

Machine Learning Models in NLP

Machine learning models are algorithms that can be used in natural language processing so that the computer can understand, analyze, generate, and manipulate human language. A few examples that you’ve probably encountered in terms of machine learning in NLP in action include: auto-correct, information extraction, email spam filters, and predictive text, to name a few. 

Deep Learning and Neural Networks

Within the umbrella of machine learning is deep learning, which utilizes neural networks to mimic the human brain. Deep learning empowers machines to grant responses that are more human-like or even to create content themselves. Deep learning makes it possible to understand very complex and intricate language by employing layers of neural networks (input, hidden, output layers). Over time, the neural networks improve with use, much like how our pathways in our brains become faster at processing information over time by forming new connections. 

Rule-Based vs Statistical NLP

There are also different types of algorithms and methods in machine learning that make NLP work. For example, rule-based rely on rules to encode the linguistic knowledge about a specific language and its grammar. They may know prefixes, suffixes, and word endings. On the other hand, statistical approaches leverage large amounts of data and analysis to interpret language. They tend to be more flexible and capable of processing complex language. 

If you were to draw concentric circles, moving from the largest to its core in order, you’d be able to categorize these terms as follows: artificial intelligence > machine learning > deep learning. 

Major Components of NLP

There’s a lot that goes into making NLP work and accurately reflect what people are saying for the computer to respond or take action. 

A few of these components are: 

Speech Recognition and Text-to-Speech Systems

Text-to-speech systems translate information into audio, speech, or voice to respond to human queries and statements. To work, a block of text passes through an NLP engine, which processes each word and generates an accurate response. TTS can be used on websites, apps, within contact centers, and more. 

Named Entity Recognition (NER)

Named entity recognition (NER) is a subset of natural language processing that is concerned with identifying and classifying data points within text. Named entities can be phrases, sequences of words, or single words in which the system will categorize them into their predefined groups. 

Part-of-Speech Tagging and Parsing

Part of speech tagging, or POS tagging, is an aspect of NER in which words within text are labeled based on their part of speech (i.e. adjectives, nouns, or verbs). 

Applications of NLP in AI

So, with all the technicalities defined, why is AI natural language processing so important and vital for businesses to use? 

NLP offers a wide variety of use cases, many which have become the new norm for everyday life. 

Take for example the following: 

Chatbots and Conversational Agents

AI-powered chatbots get better with every interaction that they have because of machine learning. They’re ubiquitous nowadays for efficient customer service. 

Automated Text Summarization and Translation

Automated text summarization does what it sounds like- summarizes text and the most important information to save humans time from having to read through long papers, news content, or legal documentation, for example. 

Information Retrieval and Extraction

Text extraction and information retrieval is extremely valuable to pull data from support tickets rather than having to go through each one. Or, you can use it to design triggered workflows without any need for human intervention. 

Natural language processing is also what powers speech-enabled automation technologies like aiOla. aiOla is unique because of its ability to understand human speech in any language, accent, and acoustic environment, but most critically, aiOla understands business-specific jargon. As a result, any business can leverage this speech tool to help complete critical workflows with nothing more than words– no learning curve or IT required! 

The Role of NLP Across Industries

Across industries, AI natural language processing is helping to save time, lower costs, reduce errors, improve collaboration, and better service customers. From healthcare to aviation, pharmaceutical to ecommerce, and everything in between, AI NLP is being used to improve:

  • Computer assisted coding (CAC)
  • Clinical diagnosis
  • Stock price prediction and credit scoring
  • Insurance claims management
  • Fraud detection
  • Auditing and reporting
  • Chatbots and customer service
  • Spam detection 
  • Autocorrect and translation 
  • Autocomplete
  • And more! 

Interested in learning how an AI NLP tool like aiOla can improve your business’ processes? We’re ready to discuss the potential!

Closing Words

AI natural language processing is a gamechanging function of artificial intelligence that allows computers and systems to understand and interact with humans linguistically. By 2032, it is predicted that the global NLP market will reach $91 billion, growing at a CAGR of about 27% between 2023 and 2023. These figures are evidence that NLP is changing how businesses function and how humans interact with machines. Since technology innovates exponentially, it begs the question, “What’s next?”