Natural Language Processing (NLP) is an important technology used by many companies today. It enables computers to understand human language and process it as data. But what is it used for exactly? In this article, we’ll look at some examples of Natural Language Processing use cases and how NLP has been applied in different industries.
NLP use case examples
With the help of NLP technology, computers now can automatically handle natural human languages like speech or text, and although this is quite fascinating in itself, the real value behind this technology lies in its use cases.
Let’s go through some real-life applications of Natural Language Processing technology:
The best spam detection technologies use NLP capabilities to scan emails and identify junk mail thanks to language that often indicates spam or phishing.
If you use Gmail, you’ll have noticed by now that our inbound emails automatically get classified into our primary inbox, promotions, and spam inbox.
This is done thanks to NLP. The AI is trained to identify and classify emails in these categories thanks to its comprehension of the content of the emails. As we’ve seen before, spam mail tends to have unclear messages and irrelevant outbound links. Similarly, promotional emails use specific language and tend to have promotional content, like coupons or discounted offers.
Grammar correction tools
Grammar correction tools, such as Grammarly, use NLP techniques in order to scan a text, check for language errors, and give suggestions on which corrections should be made.
According to Grammarly, the software is fed with data about grammar rules and spelling by their team of linguists and deep learning engineers who have designed algorithms that learn the rules and patterns of good writing, by analyzing millions of sentences from research text. It also learns with data, as every time a user accepts or ignores a suggestion given by Grammarly, the AI gets smarter. Thanks to that knowledge, the tool knows how to differentiate between correct and incorrect usage, and prompts suggested amendments or corrections.
Text summarization is the process of shortening a text and generating a concise summary whilst retaining the core idea and message conveyed by the initial document.
Once again, NLP techniques are at work here in order to “digest” huge volumes of digital text, understand the content, extract the most central ideas while ignoring irrelevant information, and create a shorter piece of text that still contains all the key points.
There are two main methods to summarize texts:
- Extractive method
In this method, algorithms use meaningful sentences and phrases from the original text and combine them to create a summary. To do so, the algorithm uses word frequency, the relevance of phrases, as well as other parameters.
- Abstractive method
In this more advanced method, the algorithm has to understand the general meaning of sentences and interpret the context in order to generate new sentences based on the overall meaning. The output is therefore a new text, completely different from the source content.
One of the top use cases of Natural Language Processing is translation. Since its inception in the 1950s, automated translation has come a long way.
An effective translation is more than just replacing words, it needs to accurately capture the meaning and tone of the input language to be able to translate it into another language with the same meaning and desired impact.
Automated translation services such as Google Translate or DeepL leverage the power of NLP to understand and produce an accurate translation of global languages in text, or even voice formats. At Inbenta, we use the power of NLP applied to automated translation in our multilingual chatbots, to ensure that our users get the answers they’re looking for in their preferred language.
Sentiment analysis tries to gauge the overall mood of a text or document, by analyzing the language used in these contents. It can be used for social media posts, responses, reviews, and more to identify the feeling, opinion, or belief of a statement, thus providing a lot of information about customers’ choices and their decision drivers.
Virtual agents and chatbots
Thanks to NLP technology, chatbots have become more human-like. Conversational AI solutions like AI-powered intelligent chatbots use Natural Language Processing to understand the meaning behind the user’s queries and answer them in an accurate way.
Chatbots have numerous applications in different industries as they facilitate conversations with customers and automate various rule-based tasks, such as answering FAQs or booking flights. They are cost-effective, and available 24/7 every single day of the year, enabling users to find answers to their questions on their own, thus enhancing the user experience.
NLP industry-specific use case examples
Natural Language Processing has become so powerful in recent years that it is now impacting business operations across various industries. Here are some of the top use cases of NLP in different sectors.
Retail & e-commerce NLP use cases
Retailers can use NLP to analyze customer data and transform it into actionable insights in order to make more informed decisions across their processes, from product design and inventory management to sales and marketing initiatives.
Marketers can extract data from various sources such as reviews, comments, social media posts, etc, and combine it with NLP capabilities to analyze consumer sentiments, detect market trends, and optimize their marketing strategies.
NLP-powered semantic search engines enable online retail stores and e-commerce websites to understand the shoppers’ intent, even when they use long-tail searches such as “black women dress size 10”, in order to suggest fitting responses and increase the visibility of products. Leveraging semantic search enables e-commerce sites to increase conversion rates and decrease cart abandonment rate.
Chatbots in e-commerce use NLP in order to understand shoppers’ queries and answer them in the most accurate way. They can even offer transactional capabilities, enabling users to find the products they are looking for, suggesting related products, promoting offers, and even finalize sales without having to leave the chatbot.
Banking and Finance NLP use cases
Banking and financial institutions can use NLP to analyze market data and use that insight to reduce risks and make better decisions. NLP can also help these institutions identify illegal activities like money laundering and other fraudulent behavior.
Banks and financial institutions use credit scoring in order to determine the risks associated with lending money to an individual or a business. NLP can assist in credit scoring by extracting relevant data from unstructured documents such as loan documentation, income, investments, expenses, etc, and feed it to credit scoring software to determine the credit score.
Combined with Artificial Intelligence, NLP can help detect fraud from unstructured financial documents.
Insurance NLP use cases
Insurance companies can use NLP to analyze customer communication to identify indicators of fraud and flag these claims for deeper analysis.
Healthcare NLP use cases
NLP can analyze patient communication from emails, chat applications, and patient helplines and help medical professionals prioritize patients based on their needs, improving patient diagnosis and treatment, and driving better outcomes.
Physicians use voice recorders to document clinical procedures and results. NLP can be used to analyze voice records and transcribe them into text, in order to be fed to patients’ records.
Healthcare chatbots use NLP capabilities to understand the patients’ queries and can assist them in scheduling appointments, locating healthcare services, assessing symptoms, setting vaccination reminders, and even providing mental health assistance or information about Covid or other public health concerns.
HR NLP use cases
NLP is also vastly used by HR departments in order to automate various tasks.
NLP can be used to screen candidates’ resumes by extracting relevant keywords (education, skills, previous roles), and to classify candidates based on how their profile matches a given position. It can also be used to summarize the resumes of candidates who match specific roles in order to help recruiters skim through resumes faster.
Chatbots for recruitment purposes are used to automate communication between recruiters and candidates. They usually use NLP capabilities in order to schedule interviews, answer candidates’ questions about the position or recruitment process, or even facilitate onboarding.
Now that you know how powerful NLP applications can be, you might want to try them out for yourself. Benefit from our 14-days FREE trial and test our conversational AI solutions for your business.
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