We don’t regularly think about the intricacies of our own languages. It’s an intuitive behavior used to convey information and meaning with semantic cues such as words, signs, or images. It’s been said that language is easier to learn and comes more naturally in adolescence because it’s a repeatable, trained behavior—much like walking. And language doesn’t follow a strict set of rules, with so many exceptions like “I before E except after C.”
What comes naturally to humans, however, is exceedingly difficult for computers with the amount of unstructured data, lack of formal rules, and absence of real-world context or intent. That’s why machine learning and artificial intelligence (AI) are gaining attention and momentum, with greater human dependency on computing systems to communicate and perform tasks. And as AI gets more sophisticated, so will Natural Language Processing (NLP). 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. Here are a few prominent examples.
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 recognizes 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.
Smart assistants like Apple’s Siri and Amazon’s Alexa recognize 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.”
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 wizard.
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 recognizes 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.
Things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. Autocomplete and predictive text are similar to search engines in that they predict things to say based on what you type, finishing the word or suggesting a relevant one. And autocorrect will sometimes even change words so that the overall message makes more sense.
They also learn from you. Predictive text will customize itself to your personal language quirks the longer you use it. This makes for fun experiments where individuals will share entire sentences made up entirely of predictive text on their phones. The results are surprisingly personal and enlightening; they’ve even been highlighted by several media outlets.
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 recognize the language based on inputted text and translate it.
Digital phone calls
We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails. Turns out, these recordings may be used for training purposes, if a customer is aggrieved, but most of the time, they go into the database for an NLP system to learn from and improve in the future. Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information. This is a NLP practice that many companies, including large telecommunications providers have put to use.
NLP also enables computer-generated language close to the voice of a human. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment.
Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations. One example is smarter visual encodings, offering up the best visualization for the right task based on the semantics of the data. This opens up more opportunities for people to explore their data using natural language statements or question fragments made up of several keywords that can be interpreted and assigned a meaning.
Applying language to investigate data not only enhances the level of accessibility, but lowers the barrier to analytics across organizations, beyond the expected community of analysts and software developers.
To learn more about how natural language can help you better visualize and explore your data, check out this webinar.
Text analytics converts unstructured text data into meaningful data for analysis using different linguistic, statistical, and machine learning techniques.
While sentiment analysis sounds daunting to brands--especially if they have a large customer base--a tool using NLP will typically scour customer interactions, such as social media comments or reviews, or even brand name mentions to see what’s being said. Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience.
Additional ways that NLP helps with text analytics are keyword extraction and finding structure or patterns in unstructured text data.
There are vast applications of NLP in the digital world and this list will grow as businesses and industries embrace and see its value. While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation.