Top 5 NLP Applications in 2024

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI). You come across NLP applications everywhere — from your phone’s voice assistant to the software programs that process unstructured data for business insights. As AI continues to advance, NLP machine learning is gaining momentum. If these NLP applications have piqued your interest, you must keep reading. Let's explore how well machines are learning to talk to humans!

What is NLP?

Machines use NLP techniques to understand, analyze, and manipulate human language. As machines become more intuitive about human communication, data processing becomes more efficient. NLP coaching can help machines understand nuances of language such as the following: 

• Sentiments

• Tone

• Opinions

NLP utilizes computational linguistics to analyze and synthesize human language in real-time. The most significant advantage is that you can analyze complex and unstructured data quickly. Machines understand and analyze different dialects and languages in an unbiased manner. This has many applications in education, healthcare, business, etc.

Top 5 NLP Applications

Here are some of the ways NLP is helping us automate and speed up everyday tasks:

1. Email Filtering

You receive piles of emails every day. How do you start sorting? The email could be useless spam or an acceptance letter from your dream college. But email filtering has evolved beyond the spam filters. Email classification in Gmail divides messages into three categories: primary, social, and promotions. This is where Natural Language Processing steps in to help. NLP identifies incoming emails and sends them to their designated folders. How does NLP work in email classification? The email service uses natural processing to extract common patterns and phrases. So the NLP model searches the content of each email to put it in the right section. There you go! Now you can review and respond to emails much quicker. And delete redundant messages to manage inbox size.

2. Voice Assistants

Are Alexa and Siri examples of NLP? Yes! You can talk to these smart assistants thanks to Natural Language Processing. The voice assistant breaks down what you say into the speech, root stem, and other linguistic features. Then it infers the meaning and naturally replies to you. Voice assistants will soon become the primary communication channel between humans and the internet. This will help users and businesses alike, because this conversational method of exploring products and services will bring customers to the right target.

3. Search Engine Autocomplete

Predictive text and auto-correction are a blessing in the world of online searches. What do you do when you have many sources on the internet but don’t know exactly what you want? NLP models can help detect the intent behind your search. And voila! You now have the right keyword. 

Moreover, there are suggested searches under your desired search that can help you explore interconnected subjects. When you start typing, the NLP model looks at the whole picture and similar search behaviors to give you these suggestions. For example, if you put in a flight number, it will show you the flight status.

4. Chatbots

Chatbots are capable of understanding as well as learning from human conversations. This is the best part of an AI-powered Chatbot: it will learn over time. You can even have extensive discussions with chatbots. They work in three simple steps: 

• Comprehend the meaning of the question asked

• Collect the required information from the user

• Provide the appropriate response

Responses provided by chatbots are friendlier and more natural than those of search engines. This is because chatbots have emotional intelligence. As a result, they have eased the customer support process for businesses. Chatbots can handle most customer queries, speeding up response time and providing 24/7 availability for users. With no wait for the customer, they can get prompt solutions to their problems, and support agents no longer need to answer repetitive questions anymore.

5. Text Analytics

Different linguistic, statistical, and machine-learning techniques are used to convert piles of unstructured data into meaningful data. This sounds complex to imagine, but NLP makes this process much easier. Businesses can examine customer behavior by going through the following sources:

• Social media comments

• Reviews

• Brand name mentions

Monitoring these behaviors can help the brand plan their next marketing campaign. The text analysis leads to keyword extraction and pattern recognition. These kinds of intricate, tedious tasks can only be performed by automation. How does the system recognize emotions and nuances in opinion? The answer is sentiment analysis. This natural processing model goes beyond the literal meaning. Using sentiment analysis, you can enhance market research to identify trends and prospects for your business. It can also help you identify customer pain points and monitor competitors’ strategies. Beyond private business uses, government agencies can also use NLP text analytics to monitor threats to state security.

Understand Natural Language Processing with Machine Learning

As you have seen, NLP techniques can automate tedious daily tasks, saving time and helping to detect patterns in data that human beings cannot. In short, Natural Language Processing is revolutionizing how we analyze data and the way machines communicate with us. 

All these exciting applications must have sparked your creativity. Do you want to learn the details of semantic analysis and machine translation? Our Natural Language Processing with Machine Learning course is an interactive way to learn how to solve day-to-day NLP problems. Try it out for yourself and learn how to make NLP work for you!

Happy learning!