Natural Language Processing Applications
* Before reading through our piece on NLP applications, make sure to check out the previous installment of this series: “What is Natural Language Processing.”
What’s this about: Natural language processing (NLP) is one of the most widely used subfields of artificial intelligence (AI) thanks to its incredible ability to analyze and manipulate human language. Whenever you are interacting with chatbots and digital assistants, which are increasingly becoming part of our everyday lives, NLP is at play. However, its real-world applications don’t stop there as NLP is utilized in a wide range of industries like finance, healthcare, legal, insurance, and autonomous vehicles.
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With NLP, machines are trained to understand how human language works, and they do this by analyzing and processing massive amounts of data. NLP’s abilities have come a long way in a short time, and it makes our technological lives far more easy and efficient. Many people might not realize that when you begin typing a question into Google, and the search engine magically finishes it for you, that is the work of NLP.
Industries Disrupted by NLP
Let’s take a look at some of the industries being disrupted by NLP:
Finance: Investment banks and financial institutions have already been adopting AI and machine learning for quite some time, but NLP is becoming increasingly implemented into processes. Large banks with an online presence are utilizing NLP for virtual assistants and chatbots, which are used for tasks like balance inquiries, account details, and more. Natural language generation (NLG) is also enabling these institutions to convert large financial datasets into written narratives. Finance teams often spend days writing about monthly financial reports given the massive amounts of data required, but efficient NLG models can do the same job in mere seconds.
Healthcare: Healthcare systems all around the world are being disrupted by NLP. More specifically, a transition is taking place from paper records to electronic medical records (EMR). These EMRs provide a lot of data that can be used for analytics-driven decisions, and healthcare organizations are using NLP to gain deep insight into health records.
Image: McKinsey & Company “Natural language processing (NLP) in healthcare”
Legal: NLP is drastically changing how lawyers work, given how their jobs often involve time-consuming and monotonous tasks, which AI is extremely efficient at. Lawyers can use NLP to extract meaning from data, documents, and other content very quickly, which can help build connections between documents and identify various patterns. NLG tools are also used in the industry, and they are especially useful for drafting basic versions of contracts, while NLP-automation tools can review a single or thousands of contracts on file. One of the greatest opportunities for NLP in the legal industry is the ability to bring legal support to many more people, since the technology can greatly reduce costs.
Image: Accenture “Top Natural Language Processing Applications in Business”
Insurance: The insurance industry has been increasingly implementing NLP to achieve better efficiency gains. Insurance companies work with many unstructured documents when processing insurance claims, and NLP can streamline the process. Supervised learning algorithms are able to process documents and provide a probability of a claim being granted.
Autonomous Vehicles: NLP is helping drivers develop a deeper trust for autonomous vehicles. It enables drivers to talk to the vehicle, which can include anything from requesting a specific destination to stopping at a restaurant. This is especially useful for certain groups of individuals, such as the elderly, who often have a harder time interacting with autonomous systems.
These are just some of the many industries being disrupted by NLP, which will only continue to impact processes from top to bottom.
Real-World Applications of NLP
Artificial intelligence continues to evolve at an incredible pace, and as it does, NLP also becomes more sophisticated. There is a good chance that you don’t realize the extent to which NLP is already implemented into your daily life, and within a short amount of time, it will be nearly impossible to go about your day without encountering it at some point.
Here is a look at some of the ways NLP is used in the real world:
Search Engines: NLP is what enables search engines to finish phrases and provide relevant results based on similar searches. However, NLP takes this even further. Search engines like Google don’t just analyze the specific search words, but rather a wider picture that includes user intent.
Email Filters: These were one of the first applications of NLP online, and while email filters used to be fairly basic, they are now far more complex. For example, Gmail utilizes NLP for email classification, labeling emails as either primary, social, or promotions based on the content. This creates an organized and manageable inbox for users with relevant emails standing out.
Voice Assistants: One of the fastest-growing areas of NLP, voice assistants are already commonplace. Some of the most popular include Apple’s Siri and Amazon’s Alexa, which can infer meaning from human language, but there are countless others on the market as well. Voice assistants are quickly evolving and becoming even more complex, with many being able to pick up on contextual clues, respond with humor and other human emotions, and develop more personal connections with us.
Predictive Text: Another one of the areas of NLP that we encounter every day is predictive text, which is present in things like autocorrect on our smartphones. It is hard for us to imagine what it was like before autocorrect, since it has become so ingrained in our communications. Predictive text can also customize itself to your language behaviors, and it becomes more accurate as time goes on.
Personality Analysis: NLP can also be used for personality analysis from personal open-source data. Social media platforms contain massive amounts of data that provide insight into individuals’ personalities, and algorithms can extract meaning from texts for a personality prediction system.
Sentiment Analysis: NLP tools enable companies to analyze customer interactions, such as reviews, social media comments, and brand name mentions to gain insight into the emotional tone and opinions of customers. These can then be used to determine the efficiency of things like marketing campaigns, and it helps improve customer experience.
Image: Accenture “Top Natural Language Processing Applications in Business”
One important aspect to remember about NLP is that it is highly language-dependent. This means that a model developed for the English language won’t work for French. Because of this, experts must create new models for every language, demonstrating once again how complex human language is in general. It is also another reason why the incredible advancement of NLP technology is so impressive.
NLP is Changing Our Everyday Lives
NLP is one of the most revolutionizing AI technologies, largely due to the fact that it is one of the first to be implemented in our everyday lives. Despite its many great accomplishments, NLP is still rapidly evolving and continuing to improve. As we already know, communication is far more than just words. It involves things like context and body language, which is why the ability of a machine to interpret such language is so revolutionizing.
We are approaching a time when it will be nearly impossible to go about your day without interacting with such a machine. NLP is dramatically altering virtually all industries, including healthcare, business, manufacturing, legal, travel, finance, and many more. NLP will only continue to make our daily lives easier through technology like virtual assistants, and it is enabling stronger relationships between companies and customers. All of this is helping both individuals and companies avoid the monotonous and time-consuming tasks involved in daily life, and it is freeing us up to focus on more important areas, which is arguably the most beneficial aspect of AI in general.