What is NLP?
You’ve probably heard the term natural language processing (NLP) by now, as it’s become a bit of a buzzword. But do you actually understand what it is? How about where it’s used? And how it affects you? If the answer to any one of those is ‘no’, don’t worry, you’re not alone. In fact, over the past five years, Google Trends shows the US searches for “what is natural language processing” soared by 300%.
So let’s start with a definition. Natural language processing makes human language ‘understandable’ to computers. It’s a type of artificial intelligence that draws on insights from computational linguistics and computer science to process and analyze spoken or written language. While that might all sound pretty technical, it’s actually already part of our everyday lives.
Where is NLP used and why?
Search engines, speech-to-text, Alexa and Siri, chatbots, autocomplete – they’re all driven by natural language processing. It’s what allows computer programs to interpret what you type or say and respond accordingly. And it’s come a long way. Just think of how we use search engines now, speaking or typing in questions and entire phrases. Compare that to the early days of the internet when you had to know a website’s exact URL to find it.
Sentiment analysis is another key application of NLP. This is where companies analyze large volumes of text, such as customer reviews, social media posts, and customer support requests to understand what people are saying about their business, product, or service. This data is then categorized, showing how many customers are happy or unhappy, and which aspects they are satisfied with or otherwise. Sentiment analysis is an incredibly useful tool and is widely used to help businesses keep their finger on the pulse of customer opinion.
NLP is important because it allows us to make sense of and harness the large amounts of data we’ve accumulated. We can use the insights it provides to make strategic business and product development decisions, monitor our brand reputation, identify and resolve issues quickly. And that’s before we even start thinking about NLP’s most direct application to the translation and localization industry: machine translation.
Why is NLP relevant for translation and localization?
NLP powers neural machine translation engines like DeepL and Google Translate. And as we in the language industry are well aware, machine translation (MT) has improved significantly in recent years and shows no sign of slowing down. We already have customized MT engines, where institutions or organizations invest in tailoring and training systems on their texts and terminology to improve performance, as with the EU’s MT service.
Then there’s adaptive MT, where systems learn from corrections and changes in real-time. Here, the translator’s post-edits are used to improve MT output. In fact, adaptive MT can even learn to adopt the translator’s writing style, as with LILT’s pioneering technology.
What does NLP hold for the future?
So where will NLP go next?
Some predict that the subfield of natural language understanding will grow in popularity, going further than NLP to “interpret intent, resolve content and word ambiguity”. This will give us a greater understanding of the nuance of human language across multiple contexts. A development that would no doubt improve machine translation performance even further.
Others believe NLP will transform how companies interpret customer sentiment, tailoring products and services to truly meet their needs. Such a shift could have implications for translation and localization as well, as businesses seek to deliver excellence on all fronts.
The future is far from clear-cut. But what does seem certain is that natural language processing will continue to be used extensively in our daily lives and in the translation industry as a whole.