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Understanding Natural Language Processing (NLP)

Here’s a broad definition of natural language processing (NLP): An automatic natural language manipulation (like text and speech) accomplished by software. Of course, there's a lot more to it, but that's it in a nutshell. NLP works to create machines that can comprehend voice data or text and also respond to it with speech or text of their own. Like humans.

For more than 50 years, the study of natural language processing has been around. With the rise of computers, it grew out of the field of linguistics. Why is natural language processing so vital today? To gain a better understanding of NLP, we have to look at a number of topics.

Natural Language – What Is It?

Natural language for a cat would be "meowing", purring, hissing, etc. For a dog, growling, barking, whining, etc.

The way humans communicate with each other is referred to as "natural language".  Most of the time, this includes text and speech. Think about how many times a day you use text and for what:

  • Webpages
  • SMS
  • Email
  • Menus
  • Signs, and more. 

But what about speech? Possibly even more than we write, we talk to each other. Though debatable, in the minds of many, compared to learning how to write, learning how to talk is easier. Human communication relies heavily on text and voice.

NLP – What Is It?

A branch of computer science (AI or artificial intelligence), NLP refers to giving computers what they need to understand spoken words and text like humans. It combines rule-based modeling of human language (computational linguistics) with machine learning, statistical, and deep learning models. Computers are enabled by these technologies working together to process the language of humans in the form of voice data or text. What's more, with the writer’s or speaker’s sentiment and intent, they must understand its full meaning.

Computer programs are driven by NLP to take text and translate it from one language to another, summarize large-volume texts, respond to spoken commands, etc., in real-time. You've likely had an interaction or two or more with NLP as the following:

  • Customer service chatbots
  • Speech to dictation software
  • Digital assistants
  • Voice operated GPS systems 
  • Various consumer conveniences 

However, business operations are also being streamlined by NLP’s growing enterprisal solutions role. Businesses rely on it to simplify mission-critical business processes, increase employee productivity, and more.


It is incredibly difficult to create software that can accurately figure out the meaning of voice data or text, in part, because of all the ambiguities filling the human language. So a computer can make sense of what's ingested, NLP tasks break down human voice and text data. Here are some of those tasks:

  • Natural language generation
  • Sentiment analysis
  • Co-reference resolution
  • NER or named entity recognition
  • Word sense disambiguation
  • Part of speech tagging
  • Speech recognition 


The following NLP approaches and tools are used in specific situations:

  • Deep Learning, Machine Learning, and Statistical NLP
  • Python and The NLTK (Natural Language Toolkit) 

Cases In Which NLP Is Useful

In many modern real-world applications, the driving force behind machine intelligence is NLP. Some examples are as follows:

  • Text summarization
  • Social media sentiment analysis
  • Virtual agents and chatbots
  • Machine translation
  • Spam detection 

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