Language is an essential part of our most basic interactions. The same is also true of technology. At the intersection of these two phenomena lies natural language processing (NLP) — the process of breaking down language into a format that is understandable and useful for both computer systems and humans.
With the increased use of AI technology, NLP is now benefiting from a similar popularity. You may not realize it, but there are countless real-world examples of NLP technology that impact our everyday lives. The following are a few of the more common applications.
If you’ve used an email account any time in the last 10 years, you’ve undoubtedly benefitted from NLP technology. Once an algorithm is sufficiently trained on email textual data, it can successfully identify, categorize, and label emails as regular emails, spam emails or malicious emails — the latter of which, is usually removed from your account before you even seen it. Similarly, other email filter types such as social or promotional may be used, depending on the email provider.
Even the business sector is realizing the benefits of this technology, as 35 percent of surveyed companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data.
For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurate translate text.
Now, thanks to AI and NLP, algorithms can be trained on texts of different languages, making it possible to produce the equivalent meaning in another language. This technology even extends to non-Romance languages like Russian and Chinese which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters, respectively.
Online search is now the primary way for people to access information. As such, more companies are realizing the value of integrating NLP search capabilities into their software. . Google recently launched a search engine that helps users get answers to complex questions like “How should I prepare to climb K2?” in text or visual format.
Likewise, leading NLP leaders such as expert.ai are also harnessing the power of search. Using a cognitive search engine (i.e., AI-enabled) customers can create bespoke enterprise search solutions like those that allow search via message or search for documents related to a specified topic. For example, the keyword “car” would also return documents about SUVs, station wagons and corvettes, as they all relate to “cars”.
Arguably the best-known example of NLP, smart assistants such as Siri, Alexa and Cortana have become increasingly integrated into our lives. Using NLP, they break language down into parts of speech, word stems and other linguistic features. Then natural language understanding (NLU), which is what allows machines to understand language, and natural language generation (NLG), the part that give machines the ability to “speak”, do the rest. Ideally, this gives you your desired response.
NLP-powered chatbots, another form of smart assistant, work the same way but, instead of using voice recognition, they reply to textual input from customers. Given their value as an informational resource, most online companies now feature them as a primary communication tool on their website.
The Digital Age has made many aspects of our day-to-day lives more convenient. As a result, consumers expect far more from their brand interactions — especially when it comes to personalization. Media organizations, which have struggled to retain readership and subscribers, have taken note and are turning to NLP for relief.
Expert.ai’s NLP platform gives publishers and content producers the power to automate important categorization and metadata information through the use of tagging, creating a more engaging and personalized experience for readers. Media outlets can also make content recommendations, so users only see the content that is most relevant them.
Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights. As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features such as named entity extraction give users the power to identifies key elements like names, dates, currency values, and even phone numbers.
Then, the entities are categorized according to predefined classifications so this important information can quickly and easily be found in documents of all sizes and formats, including files, spreadsheets, web pages and social text. Industries such as insurance even use NLP text analytics to inform decision making on claims and risk management.
The applications for NLP are broad and powerful, and we’re likely to see even more uses of this cutting-edge technology in the years ahead. What innovative NLP technology do you think will be next?