Natural Language Processing (NLP) is at work all around us, making our lives easier at every turn, yet we don’t often think about it. From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging.
NLP also benefits your business. It helps save time on monotonous, low-skill tasks and can help cut costs. This means you can put more effort and resources into improving your products, processes and profits.
In this piece, we’ll go into more depth on what NLP is, take you through a number of natural language processing examples, and show you how you can apply these within your business.
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NLP (Natural Language Processing) is an artificial intelligence technique that lets machines process and understand language like humans do using computational linguistics combined with machine learning, deep learning and statistical modeling.
Through NLP, computers don’t just understand meaning, they also understand sentiment and intent. They then learn on the job, storing information and context to strengthen their future responses.
NLP can bring a lot of benefits to your business. If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights. This could in turn lead to you missing out on sales and growth.
You also run the risk of lagging behind your competitors. IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP.
NLP is not perfect, largely due to the ambiguity of human language. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible.
Natural language processing is developing at a rapid pace and its applications are evolving every day. That’s great news for businesses since NLP can have a dramatic effect on how you run your day-to-day operations. It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers.
Ready to dive into some examples?
Here’s a list of the top 10 natural language processing examples:
Online translators are now powerful tools thanks to Natural Language Processing. But that wasn’t always the case. If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. It couldn’t be trusted to translate whole sentences, let alone texts.
Now, however, it can translate grammatically complex sentences without any problems. This is largely thanks to NLP mixed with ‘deep learning’ capability. Deep learning is a subfield of machine learning, which helps to decipher the user's intent, words and sentences.
Search engines no longer just use keywords to help users reach their search results. They now analyze people's intent when they search for information through NLP. Through context they can also improve the results that they show.
Search autocomplete is a good example of NLP at work in a search engine. This function predicts what you might be searching for, so you can simply click on it and save yourself the hassle of typing it out.
Smart search is another tool that is driven by NPL, and can be integrated to ecommerce search functions. This tool learns about customer intentions with every interaction, then offers related results.
Smart assistants, which were once in the realm of science fiction, are now commonplace.
These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation (a subfield of NLP) to answer these queries.
They are effectively trained by their owner and, like other applications of NLP, learn from experience in order to provide better, more tailored assistance.
Customer service costs businesses a great deal in both time and money, especially during growth periods. Finding ways to counter this with automation is key.
Chatbots might be the first thing you think of (we’ll get to that in more detail soon). But there are actually a number of other ways NLP can be used to automate customer service.
NLP can be used to detect sentiment and keywords in emails. These emails can then either receive automated responses or be automatically assigned to the relevant team. This means that customer emails don’t get lost in the ether, and issues are resolved promptly.
Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated. This is done by using NLP to understand what the customer needs based on the language they are using. This is then combined with deep learning technology to execute the routing.
A spam filter is probably the most well known and established application of email filters. Spam makes up an estimated 85% of total global email traffic worldwide, so these filters are essential.
But filters have also evolved as a way to help people keep their inbox organized. For instance, in gmail your emails can be sorted into primary, social, promotions, and updates.
Behind all of these filters NLP is at work. As your emails come into your inbox they are automatically scanned using text classification and keyword extraction tools, underpinned by NLP technology.
When you send out surveys, be it to customers, employees, or any other group, you need to be able to draw actionable insights from the data you get back.
However, large amounts of information are often impossible to analyze manually. Here is where natural language processing comes in handy — particularly sentiment analysis and feedback analysis tools which scan text for positive, negative, or neutral emotions.
MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results. It can sort through large amounts of unstructured data to give you insights within seconds.
Here’s an example of some analysis performed by MonkeyLearn:
Chatbots are a great way to manage your customer service queries efficiently while simultaneously lessening the load on your human team. They are available instantly, 24/7 meaning your customers might not have to wait around until an agent is ready. At the same time, this helps you cut costs. Win, win.
None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used.
People go to social media to communicate, be it to read and listen or to speak and be heard. As a company or brand you can learn a lot about how your customer feels by what they comment, post about or listen to.
However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge.
The solution? Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up.
Companies nowadays have to process a lot of data and unstructured text. Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume.
NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful.
These tools which are powered by NLP can review large amounts of data pulled from surveys, social media, emails, etc., and provide you with detailed analysis in a matter of seconds.
Plus, tools like MonkeyLearn’s interactive Studio dashboard (see below) then allow you to see your analysis in one place – click the link above to play with our live public demo.
Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes. As the name suggests, predictive text works by predicting what you are about to write. Over time, predictive text learns from you and the language you use to create a personal dictionary.
Predictive text and its cousin autocorrect have evolved a lot and now we have applications like Grammarly, which rely on natural language processing and machine learning. We also have Gmail’s Smart Compose which finishes your sentences for you as you type.
These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams.
In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence.
However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them. Natural Language Processing adoption is a must.
MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. which you can then apply to different areas of your business.
Request your free demo today to see how you can streamline your business with natural language processing and MonkeyLearn.
October 15th, 2021