Artificial Intelligence Basics: What is Natural Language Processing?

It’s #futurefridays and today we’ll be talking about the basics of Artificial Intelligence.

Previously, I talked about Computer Vision. But today we’ll tackle another field of Artificial Intelligence called Natural Language Processing.

Wikipedia defines Natural Language Processing as:

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Let me show you some examples of how Natural Language Processing is being used in real-world scenarios.

But first, I would recommend you watch this video from Accenture that gives a really simple explanation of what Natural Language Processing is, in 1 just minute.

AI 101: What is Natural Language Processing?

Video © Accenture

Now that you have a basic understanding of what Natural Language Processing or NLP is (not to be confused with Neuro-Linguistic Programming), allow me to give some examples of NLP in action today.

Predictive Text

Some of the most basic forms of NLP would have to be predictive text, including auto-correct and auto-complete.

Nowadays you can even teach predictive text new words, and based on your usage pattern, it adds it into the algorithm using machine learning.

Speech to Text

Isn’t it wonderful to have the technology nowadays where you can dictate stuff to your mobile phone, and it will type everything down for you?

What’s brilliant is that it works just as well, even if you have an accent!

Translation

Natural Language Processing has paved the way towards bridging language barriers.

A family friend recently took a trip to Japan. And they did not know how to speak a word of Japanese!

How did they get to enjoy the trip in a foreign country where you don’t understand the language? It’s all thanks to Natural Language Processing.

I’ve posted another article that talks about this in detail. I recommend you give it a read – Google Translate: Travel without Fear of Language Barriers (and How to Build your own Translator!)

OCR with Automatic Indexing and Classification

This works well with businesses that deal with documents that contain unstructured or free-format data.

You use Optical Character Recognition to convert the document into machine-readable format, then use Natural Language Processing to understand the context, and then extract the necessary information and/or categorize the documents accordingly.

I’ve also posted another article that talks about this in detail. Check it out – How can OCR (Optical Character Recognition) become AI (Artificial Intelligence)? Here’s how.

Sentiment Analysis

As with the example above, you can use Natural Language Processing to understand the context behind what customers are saying.

This can be used to understand customer sentiments in stuff like survey responses, feedback or complaint channels, social media, voice calls, and so forth.

Chat BOTs and Virtual Assistants

Chat BOTs and Virtual Assistants like Siri, Alexa, and Google Assistant are probably some of the most complex use cases of Natural Language Processing.

This is probably a whole topic in itself. If you’d want to find out more, let me know by replying with a comment.

Ending Note

I hope you found this to be of value, and gave you a sense of what Natural Language Processing is and where it is being utilized in real-world scenarios.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get updates on the next article, subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.