5 Natural Language Processing examples

5 Natural Language Processing examples is profiled by BTW Media because published evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.

• Natural Language Processing (NLP) stands at the forefront of cutting-edge technology, empowering machines to understand, interpret, and generate human language.
• While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives.
• From sentiment analysis and language translation to chatbots and text summarisation, the examples discussed in this blog illustrate the broad spectrum of NLP applications.

The intricacies of language often evade conscious consideration, as communication flows intuitively, relying on semantic cues like words, signs, or images to convey meaning. It has been posited that language acquisition, akin to walking, becomes more natural in adolescence through repetition and training. However, unlike rigidly governed activities such as following traffic laws, language lacks stringent rules, evident in exceptions like “I before E except after C.” While language acquisition appears effortless for humans, it presents formidable challenges for computers due to the abundance of unstructured data, absence of formal rules, and lack of real-world context or intent.

In response to these challenges, there is a growing reliance on machine learning and artificial intelligence (AI), which demonstrate an increasing capacity to handle human-computer interactions and perform tasks autonomously. As AI and augmented analytics advance, so too does Natural Language Processing (NLP). Despite perceptions of AI and NLP evoking futuristic images of robots, basic applications of NLP are already integrated into everyday life. Below are several notable examples of NLP in action.

Also read: What is a conversational AI platform?

1. Email filters

Email filters are one of the most basic and initial applications of NLP online. It started out with spam filters, uncovering certain words or phrases that signal a spam message. But filtering has upgraded, just like early adaptations of NLP. One of the more prevalent, newer applications of NLP is found in Gmail’s email classification. The system recognises if emails belong in one of three categories (primary, social, or promotions) based on their contents. For all Gmail users, this keeps your inbox to a manageable size with important, relevant emails you wish to review and respond to quickly.

Also read: What are the mechanics behind conversational AI?

2. Smart assistants

Smart assistants like Apple’s Siri and Amason’s Alexa recognise patterns in speech thanks to voice recognition, then infer meaning and provide a useful response. We’ve become used to the fact that we can say “Hey Siri,” ask a question, and she understands what we said and responds with relevant answers based on context. And we’re getting used to seeing Siri or Alexa pop up throughout our home and daily life as we have conversations with them through items like the thermostat, light switches, car, and more. We now expect assistants like Alexa and Siri to understand contextual clues as they improve our lives and make certain activities easier like ordering items, and even appreciate when they respond humorously or answer questions about themselves. Our interactions will grow more personal as these assistants get to know more about us. As a New York Times article “Why We May Soon Be Living in Alexa’s World,” explained: “Something bigger is afoot. Alexa has the best shot of becoming the third great consumer computing platform of this decade.”

3. Search results

Search engines use NLP to surface relevant results based on similar search behaviors or user intent so the average person finds what they need without being a search-term wisard. For example, Google not only predicts what popular searches may apply to your query as you start typing, but it looks at the whole picture and recognises what you’re trying to say rather than the exact search words. Someone could put a flight number in Google and get the flight status, type a ticker symbol and receive stock information, or a calculator might come up when inputting a math equation. These are some variations you may see when completing a search as NLP in search associates the ambiguous query to a relative entity and provides useful results.

4. Predictive text

Autocorrect, autocomplete, and predictive text have become so ingrained in our smartphone experience that we often overlook their presence. Similar to search engines, autocomplete and predictive text anticipate our words based on input, suggesting relevant options or completing our sentences. Autocorrect, on the other hand, occasionally alters words to enhance overall coherence. Notably, these features adapt and evolve with our usage, customising themselves to our unique linguistic patterns over time. This personalised touch often leads to amusing experiments, where users share sentences composed entirely of predictive text, offering surprisingly intimate insights into their language habits. Such phenomena have garnered attention from various media outlets, shedding light on the fascinating interplay between technology and personal expression.

5. Language translation

One of the tell-tale signs of cheating on your Spanish homework is that grammatically, it’s a mess. Many languages don’t allow for straight translation and have different orders for sentence structure, which translation services used to overlook. But they’ve come a long way. With NLP, online translators can translate languages more accurately and present grammatically correct results. This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, tools now recognise the language based on inputted text and translate it.