The most popular NLP use cases

Natural Language Processing (NLP) is an important technology used by many companies today. It allows computers to understand human language and process it as data. But what exactly is it used for? In this article, we’ll look at some examples of natural language processing use cases and how NLP has been applied in various industries.

The most common NLP use cases

Examples of NLP use cases

With the help of NLP technology, computers can now automatically process natural human languages ​​such as speech or text, and while this is quite fascinating in itself, the real value of this technology is in its use cases.

Let’s go through some real-life applications of natural language processing technology.

Spam detection

The best spam detection technologies use NLP capabilities to scan emails and identify junk emails due to language that often indicates spam or phishing.

Email classification

If you use Gmail, you may have already noticed that our incoming emails are automatically sorted into our main inbox, ads and spam inboxes.

This is done thanks to NLP. Artificial intelligence is trained to identify and classify emails. posts in these categories thanks to email. understanding of the content of letters. As we have seen before, spam mail usually has unclear messages and irrelevant outgoing links. Similarly, promotional emails use specific language and tend to have promotional content such as coupons or discounted offers.

Grammar correction tools

Grammar correction tools like Grammarly use NLP techniques to scan text, check for language errors, and make suggestions for corrections.

According to Grammarly, the software is fed data on grammar rules and spelling from their team of linguists and deep learning engineers who 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 made by Grammarly, the AI ​​gets smarter. With that knowledge, the tool knows how to distinguish between correct and incorrect usage and prompts suggested fixes or corrections.

Summary of the text

Summarizing a text is the process of shortening a text and creating a concise summary while retaining the main idea and message conveyed by the original document.

Again, NLP techniques come into play here 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 main points.

There are two main ways of summarizing texts.

  • Extraction method
    In this method, algorithms take meaningful sentences and phrases from the original text and combine them to create a summary. To do this, the algorithm uses word frequency, phrase relevance, and other parameters.
  • Abstract method
    In this more advanced method, the algorithm must understand the overall meaning of the sentences and interpret the context to generate new sentences based on the overall meaning. Therefore, the output is a new text that is completely different from the source content.

Automatic translation

One of the most popular cases of natural language processing is translation. Machine translation has come a long way since its inception in the 1950s.

Effective translation is more than just replacing words, it must accurately capture the meaning and tone of the input language in order to translate it into another language with the same meaning and desired effect.

Automated translation services such as Google Translate or DeepL use the power of NLP to understand and produce accurate translations of world languages ​​in text or even audio formats. At Inbenta, we use the power of NLP applied to automatic translation in our multilingual chatbots to make sure our users get the answers they’re looking for in their preferred language.

Sentiment analysis

Sentiment analysis attempts to measure the overall sentiment of a text or document by analyzing the language used in this content. It can be used for social media posts, responses, reviews, etc. to find out the sentiment, opinion or belief of a statement, thus providing a lot of information about customers’ choices and the motivations behind their decisions.

NLP Use Cases - Sentiment Analysis

Virtual agents and chatbots

Thanks to NLP technology, chat-bots have become more human-like. Conversational AI solutions, such as intelligent AI-powered chatbots, use natural language processing to understand the meaning of user queries and respond accurately to them.

Chatbots have many applications across 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 day of the year, enabling users to find answers to their questions on their own, thus improving the user experience.

Examples of industry-specific NLP use cases

Natural language processing has become so powerful in recent years that it is now impacting businesses across multiple industries. Here are some of the best NLP use cases in various industries.

Retail and E-Commerce NLP Use Cases

Retailers can use NLP to analyze customer data and turn it into actionable insights to make more informed decisions across their processes, from product design and inventory management to sales and marketing initiatives.

Market intelligence
Marketers can pull data from a variety of sources, such as reviews, comments, social media posts, and more, and combine it with NLP capabilities to analyze consumer sentiment, identify market trends, and optimize their marketing strategies.

Semantic search
NLP-powered semantic search engines enable online retailers and e-commerce sites to understand shopper intent, even when using long-tail searches like “black women’s clothing size 10,” to suggest relevant answers and increase visibility. of goods. Using semantic searches enables e-commerce sites to increase conversion rate and decrease cart abandonment rate.

E-commerce chatbot
In e-commerce, chatbots use NLP to understand customer queries and answer them more accurately. They can even offer transactional features, enabling users to find the products they’re looking for, suggest related products, promote offers, and even finalize sales without ever leaving 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 risk and make better decisions. NLP can also help these institutions detect illegal activities such as money laundering and other fraudulent behavior.

Credit points
Banks and financial institutions use credit scores to determine the risks associated with lending money to an individual or business. NLP can help credit scoring by extracting relevant data from unstructured documents such as loan documents, income, investments, expenses, etc., and feed it to credit scoring software to determine a credit score.

Fraud detection
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 communications to identify indicators of fraud and flag those claims for deeper analysis.

Healthcare NLP use cases

NLP can analyze patient communication via email. from emails, chat apps and patient helplines and help doctors prioritize patients based on their needs, improving patient diagnosis and treatment and delivering better outcomes.

Doctors use tape recorders to document clinical procedures and outcomes. NLP can be used to analyze voice recordings and transcribe them into text to feed into patient notes.

A healthcare chatbot
Health chatbots use NLP capabilities to understand patient queries and can help them schedule appointments, find healthcare services, assess symptoms, set vaccination reminders and even provide mental health help or information about Covid or other public health issues.

HR NLP use cases

NLP is also widely used by HR departments to automate various tasks.

NLP use cases in HR

Resume evaluation
NLP can be used to screen candidate resumes by extracting relevant keywords (education, skills, previous roles) and rank candidates based on how their profile fits the role. It can also be used to summarize the resumes of candidates who fit specific roles to help recruiters go through resumes faster.

Recruitment chatbot
For recruitment purposes, chatbots are used to automate communication between recruiters and candidates. They typically use NLP capabilities to schedule interviews, answer candidate 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. Take advantage of our 14-day FREE trial and test our conversational AI solutions for your business.

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