When Google released its Assistant tool, it was an absolute game changer. People could finally interact with their mobile devices via voice commands instead of having to type everything manually.
Instead of opening Google Maps, typing a destination, and then waiting for the results, a user could simply say, “Hey, Google, take me to the nearest restaurant.” Google Assistant would automatically open Google Maps and either show you a collection of the nearest restaurants or give you directions to the restaurant you requested.
That was a game-changer. Not only did it simplify the process, but it also made it possible for users to go fully hands-free while driving.
Of course, with the advent of AI, we’re seeing this idea taken to an entirely new level, thanks to natural language processing (NLP).
What is NLP?
Natural language processing is a subfield of AI, linguistics, and computer science that focuses on processing natural language datasets. The goal of NLP is to make it possible for computers to understand both text and spoken words in the same way a human can.
One early example of this is when Google enabled its assistant to do conversational follow-ups. Not only could you say, “Hey, Google, what’s today’s weather?” but you could also follow it up with “What’s tomorrow’s weather?” With the right subject, you could carry Google Assistant down all sorts of rabbit holes.
User: Hey, Google, who is the goalie for Man City?
Google Assistant: The goalie for Man City is Ederson Santana de Moraes.
User: What is his nickname?
Google Assistant: Ederson Santana de Moraes’ nickname is Ederson.
User: How old is he?
Google Assistant: Ederson is thirty years old.
You could continue that conversation until you ran out of questions to ask.
And that was NLP in its infancy. Today, intersecting technologies are far more advanced.
If you’re interested in numbers, Market and Markets predict the global NLP market will skyrocket to over $49 billion by 2027. That’s a remarkable CAGR of 25.7%.
If you’re unsure how NLP will continue benefiting users, just look at how quickly Google made use of it with its Assistant tool and how the advances in the technology made mobile devices exponentially easier to use.
That is the goal of NLP—to vastly improve the user experience (UX).
Think back—way back—to Star Trek IV. Remember the scene where Scotty sat down at a computer and spoke the keyword “Computer”? He expected the computer to respond to his voice because that’s what computers do. It wasn’t until he remembered he’d gone back in time to a period where technology wasn’t nearly as advanced as it was in his timeline.
That’s where we are now. Instead of Scotty (or us) having to pick up the mouse and talk into it or having to actually type at a keyboard, we simply talk to our devices, and they respond. We see this in everyday life, not just with Google Assistant and Siri but also in our automobiles. We speak commands, and the technology complies.
But NLP doesn’t want to limit itself to commands. Instead, it wants the interaction between humans and computers to be as natural as possible (similar to the conversational interaction with Google Assistant).
And NLP goes beyond the simple act of commands and conversations with a mobile device. There are a number of applications for NLP that have wide-ranging implications for both humans and technology.
Sentiment Analysis
Sentiment analysis is focused on giving technology the ability to determine the emotional tone or sentiment expressed by a human being. The applications of this are quite important to businesses, as it can empower chatbots to better determine how to help customers more effectively by judging feedback and attitudes toward a product or service.
If a customer were to say something like “Sure, your product really helped me” but does so in a very sarcastic way, it would be NLP’s goal to be able to discern that statement from an honest one.
Sentiment is a crucial aspect of customer service and support. Integrating NLP with machine learning and deep learning algorithms improves tool accuracy and enhances NLP applications, contributing to developing advanced human-centric technology. Machine learning statistical techniques can identify speech parts, entities, sentiment, and text aspects. Reach out to a Machine Learning development company to learn more about its contribution to NLP products.
Language Translation
Another very important aspect of NLP is the ability to translate languages in real-time. This is becoming more and more important as technology continues to “shrink” the world. With companies having to serve customers in all regions of the world, the ability to translate language quickly can make the difference between a good UX and a poor one.
For example, if your company is based in India and your support staff only has a limited grasp of the English language, they’ll struggle to help English-speaking users. With NLP involved, that translation is not only instant, it’s accurate.
Extracting Information
Imagine you have a trove of unstructured data, and you need to make sense of it. Not everyone has the skills necessary for such a task, but NLP certainly does. Because NLP is perfectly adept at identifying and extracting entities, relationships, and facts from documents, it makes for a great option for any business that depends on data analysis. And since NLP-powered technology is exponentially faster at extracting data than a human, it makes perfect sense for large businesses (that depend on massive amounts of data) to employ this technology.
By using NLP to extract information, your business will be better equipped to understand the needs and wants of your users and identify patterns to improve their experience. A technology closely linked to UX and UI is Java. Now, if you’re wondering what you can do with Java in this regard, know that there are several Java libraries developers can use to build chatbots, and it is possible to use Java libraries for language analysis to draw insights from outcomes.
Classification
At some point, your business will need to be able to quickly (and reliably) classify documentation so it’s easier to analyze and consume. One important example of how NLP can be used to vastly improve text classification is in spam detection. If your business provides email services for customers, their experience will be dramatically improved if you can reliably, consistently, and quickly classify any email that is spam so users don’t have to deal with it.
Unlike email clients that are less than reliable with spam detection, NLP succeeds remarkably. If you can deliver improved spam filtering to your users, their experience will be considerably improved, which will be reflected in their opinions and reviews of your product.
All of these things are a part of NLP, but at the heart and soul of this technology is language, especially of the conversational type. And NLP has advanced so far that some say the Turing Test must be modernized for 21st-century AI. The Turing Test is a test to determine a machine’s ability to exhibit intelligent behavior. NLP makes it possible to much more easily pass this test, which not only means the test might not be sufficient for modern technology, but it also means that NLP has surpassed previous incarnations of AI.
That equates to a far better UX from just about every angle. And if your company is looking to improve or adopt UX best practices, NLP might just be the technology you’ve been missing.
