Exploring Natural Language Processing Applications

Natural language processing (NLP) stands at the junction where computer science, artificial intelligence, and linguistics meet. This intriguing technology enables machines to understand, meaningfully interpret, and respond to human language, with various benefits that result. From simplifying daily tasks to revolutionizing industries, NLP applications are increasingly integral to your digital life. Additionally, you might consider working in this sector of the economy because, according to Statista, the market value of NLP is projected to be 9.71 billion USD in 2024 and is estimated to reach 41.79 billion USD by 2030 [1].

Explore how machines process and analyze human language and the myriad applications of NLP that are shaping the future of communication.

Understanding natural language processing

Before looking into specific applications, it’s essential to understand that NLP bridges the gap between the digital and human worlds, allowing machines to comprehend human language. 

Natural language, or human, ordinary language, is more complicated than the constructed languages you usually use to communicate with a computer. Even a high-level language like Python uses commands that would sound out of place in human communication. For example, if you want a list of commands you can use with Git in Python, you can enter the command prompt “git help.” If you wanted to ask a coworker to help you with a project, it would be rude to say, “Project, help.” Or, at least, your coworker might look at you strangely. Using natural language, you might say, “Can you help with this project?” 

Natural language processing allows computers to understand human speech and the intricacies, nuances, tones, and subtleties that come along with it. 

Key natural language processing applications

NLP has a wide array of applications across various sectors, such as finance, insurance, and health care. It significantly impacts how you interact with technology and how businesses process large volumes of text data. Prominent applications for NLP technology include voice-activated assistants, machine translation, sentiment analysis, chatbots and virtual customer support, classification and categorization, content recommendation systems, text summarization, speech recognition, and natural language generation. 

Voice-activated assistants

NLP powers virtual assistants, so if you ever use Apple’s Siri, Amazon’s Alexa, and IBM’s watsonx Assistant, you’ve already experienced NLP. This technology enables them to understand and respond to voice commands. It allows you to interact with your device using natural language to perform tasks, search for information, and control smart home devices.

Machine translation

NLP is the driving force behind machine translation services such as Google Translate. It allows for the automatic translation of text and speech between languages, making global communication more accessible. NLP allows an online translator to understand the individual rules of grammar and language structure between two languages and effectively decode one into the other.

Sentiment analysis

Your business can use NLP for sentiment analysis to gauge a customer’s opinion, their satisfaction, and the market’s response to your products by analyzing social media posts, customer reviews, and survey responses. This can help your company make better decisions, especially when formulating future strategies.

Chatbots and virtual customer support

NLP enables chatbots to understand and respond to customers' questions and comments in a conversational manner. This application is widely used in customer service to provide instant assistance, book appointments, and resolve common issues.

Text classification and categorization

NLP algorithms can automatically categorize and tag text into predefined categories, which is helpful in information management systems, email filtering (such as spam detection), and organizing large sets of text. Email filtering may be the most widely used tool. It identifies context clues to guess which emails may be spam and which require attention.

Content recommendation systems

Platforms like T-Mobile, Spotify, and Disney+ use NLP-based recommendation systems to analyze user preferences and provide personalized content suggestions based on previous interactions and the content's textual data. Using sentiment analysis—also powered by natural language processing—recommendation systems can even recommend movies, music, or other media based on how users have reviewed those products. 

Text summarization

Automatic text summarization tools use NLP to condense long documents into shorter summaries, preserving essential information. This is particularly useful if you must quickly digest large volumes of information.

Speech recognition

Speech recognition assists with converting spoken language into text in real-time, which is essential for dictation software, hands-free computing, and real-time transcription services. Everybody talks a little differently: At different speeds, in varying tones, with accents and regional dialects, and with differing pronunciations. After the speech recognition software transcribes your words, natural language processing analyzes those words, determines the meaning behind them, and then formulates an appropriate response.

Natural language generation (NLG)

This involves using NLP to generate natural language text from data, enabling applications like automated report generation, personalized content creation, and article writing. NLG can also craft stories. With natural language generation, you can ask an AI language model like Amazon’s Alexa or Apple’s Siri a question as if speaking to another person. The model will respond similarly. 

These applications demonstrate the versatility and impact of NLP in simplifying interactions, enhancing accessibility, and providing deeper insights from textual data across diverse domains.

NLP applications across industries

Natural Language Processing (NLP) is impacting various industries, such as health care and finance, by enhancing efficiency, improving decision-making, saving money, and helping professionals access corporate information in an entirely new way. NLP is transforming key sectors like retail, e-commerce, and education.

NLP applications in health care

In health care, three ways that professionals use natural language processing are for clinical documentation, diagnosis, and creating personalized treatment plans. 

Clinical documentation is challenging in health care because patient information and medical history can come from various formats and sources. Natural language processing can help overcome this challenge by organizing and understanding unstructured notes and presenting that information in a format doctors, nurses, and specialists can easily interact with. 

For example, a doctor could give the patient’s medical information to the AI model and talk with a large language model about the patient’s current symptoms. The AI could reference this information against the medical information in its library of training material and suggest potential diagnoses for the doctor to follow up on. The AI could then recommend a personalized treatment plan based on the nuances of the individual’s care needs. 

NLP applications in finance

The finance industry also has natural language applications. For example, if you're an investor or analyst, you can use sentiment analysis to determine how consumers feel about the economy or markets. Financial firms can also deploy cost-effective chatbots to assist customers. 

Another exciting use for natural language processing in finance is similar to the ability of a doctor or nurse to speak to a chatbot about their patient’s health care records. A financial analyst or other professional could use NLP to analyze massive textual data to look for trends, track performance, and predict how the markets will respond. 

Natural language processing applications in retail and e-commerce

In retail and e-commerce, natural language processing can help customers find the products they’re looking for and allows you to offer clients personalized shopping experiences. 

The benefits of natural language processing to the e-commerce industry might begin with search, where users can describe the item they want to purchase in many different ways. Your AI search model can use NLP to understand the context of what a customer searches for, helping them connect faster to your items. 

Natural language processing can also offer a solution for personalized shopping experiences. Users can chat with a virtual personal shopper who uses the customer’s feedback to look for items the customer might want to buy. The AI could consider your customer’s budget and style needs to suggest pieces that are more likely to appeal to your customer. It can increase conversation by removing the friction between your customers wanting to purchase and finding a great piece they love. 

NLP applications in education

While educators debate the pros, cons, and ethical implications of artificial intelligence in the classroom, natural language processing also benefits students. Natural language processing can help teachers assess learner performance, and with this information, the instructors can develop a more personalized approach for each student. Additionally, teachers can utilize NLP software to assist students in learning English and languages such as Spanish and Japanese. Finally, teachers can use natural language processing algorithms to grade essays because this technology can analyze grammar, syntax, and vocabulary.

Getting started with NLP technology on Coursera

Researchers and other innovators will continue to develop natural language processing applications as the NLP technology becomes more advanced. If you’re interested in becoming one of these innovators, you want to establish a background in linguistics, computer science, and AI. If you’re ready to take the next step forward and discover more, consider the Natural Language Processing Specialization offered by DeepLearning.AI on Coursera. Explore machine translation, transformers, sentiment analysis, attention models, and more with this four-course Specialization.