14 Natural Language Processing Examples NLP Examples

Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the 1990s. Called DeepHealthMiner, the tool analyzed millions of posts from the Inspire health forum and yielded promising results. For example, NLP can be used to analyze customer feedback and determine customer sentiment through text classification.

This data can then be used to create better targeted marketing campaigns, develop new products, understand user behavior on webpages or even in-app experiences. Additionally, companies utilizing NLP techniques have also seen an increase in engagement by customers. In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important. NLP algorithms focus on linguistics, computer science, and data analysis to provide machine translation capabilities for real-world applications.

Predictive text and its cousin autocorrect have evolved a lot and now we have applications like Grammarly, which rely on natural language processing and machine learning. We also have Gmail’s Smart Compose which finishes your sentences for you as you type. Similarly, support ticket routing, https://chat.openai.com/ or making sure the right query gets to the right team, can also be automated. This is done by using NLP to understand what the customer needs based on the language they are using. Natural language processing is developing at a rapid pace and its applications are evolving every day.

More than a mere tool of convenience, it’s driving serious technological breakthroughs. “The decisions made by these systems can influence user beliefs and preferences, which in turn affect the feedback the learning system receives — thus creating a feedback loop,” researchers for Deep Mind wrote in a 2019 study. Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant.

Kea aims to alleviate your impatience by helping quick-service restaurants retain revenue that’s typically lost when the phone rings while on-site patrons are tended to. These models can be written in languages like Python, or made with AutoML tools like Akkio, Microsoft Cognitive Services, and Google Cloud Natural Language. Every Internet user has received a customer feedback survey at one point or another. While tools like SurveyMonkey and Google Forms have helped democratize customer feedback surveys, NLP offers a more sophisticated approach. Akkio’s no-code AI platform lets you build and deploy a model into a chatbot easily.

Sentiment Analysis is also widely used on Social Listening processes, on platforms such as Twitter. This helps organisations discover what the brand image of their company really looks like through analysis the sentiment of their users’ feedback on social media platforms. Now, however, it can translate grammatically complex sentences without any problems. Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences.

Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used. Also, for languages with more complicated morphologies than English, spellchecking can become very computationally intensive. With Natural Language Processing, businesses can scan vast feedback repositories, understand common issues, desires, or suggestions, and then refine their products to better suit their audience’s needs. In areas like Human Resources, Natural Language Processing tools can sift through vast amounts of resumes, identifying potential candidates based on specific criteria, drastically reducing recruitment time.

These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used. Here is where natural language processing comes in handy — particularly sentiment analysis and feedback analysis tools which scan text for positive, negative, or neutral emotions. Roblox offers a platform where users can create and play games programmed by members of the gaming community.

Through Natural Language Processing, businesses can extract meaningful insights from this data deluge. Natural Language Processing seeks to automate the interpretation of human language by machines. When you think of human language, it’s a complex web of semantics, grammar, idioms, and cultural nuances.

When integrated, these technological models allow computers to process human language through either text or spoken words. As a result, they can ‘understand’ the full meaning – including the speaker’s or writer’s intention and feelings. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses.

At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans. Social media monitoring uses NLP to filter the overwhelming number of comments and queries that companies might receive under a given post, or even across all social channels. These monitoring tools leverage the previously discussed sentiment analysis and spot emotions like irritation, frustration, happiness, or satisfaction.

In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products. Thanks to NLP, businesses are automating some of their daily processes and making the most of their unstructured data, getting actionable insights that they can use to improve customer satisfaction and deliver better customer experiences. Here, NLP breaks language down into parts of speech, word stems and other linguistic features. Natural language understanding (NLU) allows machines to understand language, and natural language generation (NLG) gives machines the ability to “speak.”Ideally, this provides the desired response.

The evolution of natural language processing

That’s why smart assistants like Siri, Alexa and Google Assistant are growing increasingly popular. Interestingly, the Bible has been translated into more than 6,000 languages and is often the first book published in a new language. A slightly more sophisticated technique for language identification is to assemble a list of N-grams, which are sequences of characters which have a characteristic frequency in each language.

Customer service costs businesses a great deal in both time and money, especially during growth periods. Smart assistants, which were once in the realm of science fiction, are now commonplace. Natural Language Processing (NLP) is at work all around us, making our lives easier at every turn, yet we don’t often think about it. From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging.

You can then be notified of any issues they are facing and deal with them as quickly they crop up. The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks.

Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront. NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. By applying NLP techniques, companies can identify trends and customer feedback in order to better understand their customers, improve their products and services, create more engaging content, and analyze large amounts of unstructured data.

Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few. In contrast to the NLP-based chatbots we might find on a customer support page, these models are generative AI applications that take a request and call back to the vast training data in the LLM they were trained on to provide a response. It’s important to understand that the content produced is not based on a human-like understanding of what was written, but a prediction of the words that might come next.

Chatbots are common on so many business websites because they are autonomous and the data they store can be used for improving customer service, managing customer complaints, improving efficiencies, product research and so much more. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks. Chatbots do all this by recognizing the intent of a user’s query and then presenting the most appropriate response.

They’re not just recognizing the words you say; they’re understanding the context, intent, and nuances, offering helpful responses. By understanding NLP’s essence, you’re not only getting a grasp on a pivotal AI subfield but also appreciating the intricate dance between human cognition and machine learning. In this exploration, we’ll journey deep into some Natural Language Processing examples, as well as uncover the mechanics of how machines interpret and generate human language.

Make the most out of your untapped business and customer data with this guide to the nine best text classification examples. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check. Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically tagged as one with negative sentiment.

Personalized recommendations have become a major source of revenue for online retailers, as they understand that customers aren’t always looking for what’s on sale or in stock — they often want to find something that pleasantly surprises them. By analyzing billions of sentences, these chains become surprisingly efficient predictors. They’re also very useful for auto correcting typos, since they can often accurately guess the intended word based on context.

And yet, although NLP sounds like a silver bullet that solves all, that isn’t the reality. Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data. The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. To better understand the applications of this technology for businesses, let’s look at an NLP example.

Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis. Features like autocorrect, autocomplete, and predictive text are so embedded in social media platforms and applications that we often forget they exist. Autocomplete and predictive natural language processing examples text predict what you might say based on what you’ve typed, finish your words, and even suggest more relevant ones, similar to search engine results. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling.

What is Natural Language Processing?

With its focus on user-generated content, Roblox provides a platform for millions of users to connect, share and immerse themselves in 3D gaming experiences. The company uses NLP to build models that help improve the quality of text, voice and image translations so gamers can interact without language barriers. These assistants can also track and remember user information, such as daily to-dos or recent activities.

The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction. NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. Search engines no longer just use keywords to help users reach their search results. The proposed test includes a task that involves the automated interpretation and generation of natural language.

Whether reading text, comprehending its meaning, or generating human-like responses, NLP encompasses a wide range of tasks. Top word cloud generation tools can transform your insight visualizations with their creativity, and give them an edge. Repustate has helped organizations worldwide turn their data into actionable insights. Learn how these insights helped them increase productivity, customer loyalty, and sales revenue.

Businesses use large amounts of unstructured, text-heavy data and need a way to efficiently process it. Much of the information created online and stored in databases is natural human language, and until recently, businesses couldn’t effectively analyze this data. A natural language processing expert is able to identify patterns in unstructured data. For example, topic modelling (clustering) can be used to find key themes in a document set, and named entity recognition could identify product names, personal names, or key places.

• Since you don’t need to create a list of predefined tags or tag any data, it’s a good option for exploratory analysis, when you are not yet familiar with your data.
• As NLP evolves, smart assistants are now being trained to provide more than just one-way answers.
• MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis.
• Here, NLP breaks language down into parts of speech, word stems and other linguistic features.
• If a negative sentiment is detected, companies can quickly address customer needs before the situation escalates.
• Our eyes and ears are equivalent to the computer’s reading programs and microphones, our brain to the computer’s processing program.

SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge. While there are many challenges in natural language processing, the benefits of NLP for businesses are huge making NLP a worthwhile investment. NLP tools process data in real time, 24/7, and apply the same criteria to all your data, so you can ensure the results you receive are accurate – and not riddled with inconsistencies. A chatbot system uses AI technology to engage with a user in natural language—the way a person would communicate if speaking or writing—via messaging applications, websites or mobile apps. The goal of a chatbot is to provide users with the information they need, when they need it, while reducing the need for live, human intervention.

Read on to learn what natural language processing is, how NLP can make businesses more effective, and discover popular natural language processing techniques and examples. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights. As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features, such as named entity extraction, give users the power to identify key elements like names, dates, currency values, and even phone numbers in text. Expert.ai’s NLP platform gives publishers and content producers the power to automate important categorization and metadata information through the use of tagging, creating a more engaging and personalized experience for readers. Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them.

Using NLP to get insights out of documents

This is NLP in action, continuously learning from your typing habits to make real-time predictions and enhance your typing experience. We were blown away by the fact that they were able to put together a demo using our own YouTube channels on just a couple of days notice. Natural Language Processing enables you to perform a variety of tasks, from classifying text and extracting relevant pieces of data, to translating text from one language to another and summarizing long pieces of content. So for machines to understand natural language, it first needs to be transformed into something that they can interpret. All this business data contains a wealth of valuable insights, and NLP can quickly help businesses discover what those insights are.

Translation applications available today use NLP and Machine Learning to accurately translate both text and voice formats for most global languages. Autocorrect can even change words based on typos so that the overall sentence’s meaning makes sense. For example, over time predictive text will learn your personal jargon and customize itself. It might feel like your thought is being finished before you get the chance to finish typing.

Whenever our team had questions, Repustate provided fast, responsive support to ensure our questions and concerns were never left hanging. One of the best NLP examples is found in the insurance industry where NLP is used for fraud detection. It does this by analyzing previous fraudulent claims to detect similar claims and flag them as possibly being fraudulent. This not only helps insurers eliminate fraudulent claims but also keeps insurance premiums low.

Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents Chat PG one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). Employee-recruitment software developer Hirevue uses NLP-fueled chatbot technology in a more advanced way than, say, a standard-issue customer assistance bot.

What is natural language processing with examples?

Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices. If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page.

Imagine there’s a spike in negative comments about your brand on social media; sentiment analysis tools would be able to detect this immediately so you can take action before a bigger problem arises. Natural language processing tries to think and process information the same way a human does. First, data goes through preprocessing so that an algorithm can work with it — for example, by breaking text into smaller units or removing common words and leaving unique ones. Once the data is preprocessed, a language modeling algorithm is developed to process it.

Learn the basics and advanced concepts of natural language processing (NLP) with our complete NLP tutorial and get ready to explore the vast and exciting field of NLP, where technology meets human language. Natural Language Processing is a subfield of AI that allows machines to comprehend and generate human language, bridging the gap between human communication and computer understanding. For instance, by analyzing user reviews, companies can identify areas of improvement or even new product opportunities, all by interpreting customers’ voice. You can foun additiona information about ai customer service and artificial intelligence and NLP. By offering real-time, human-like interactions, businesses are not only resolving queries swiftly but also providing a personalized touch, raising overall customer satisfaction. Voice assistants like Siri or Google Assistant are prime Natural Language Processing examples.

Benefits of natural language processing

In contrast, machine translation allows them to render content from one language to another, making the world feel a bit smaller. Data analysis companies provide invaluable insights for growth strategies, product improvement, and market research that businesses rely on for profitability and sustainability. NLP can be used to great effect in a variety of business operations and processes to make them more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. The biggest advantage of machine learning algorithms is their ability to learn on their own. You don’t need to define manual rules – instead machines learn from previous data to make predictions on their own, allowing for more flexibility.

They are effectively trained by their owner and, like other applications of NLP, learn from experience in order to provide better, more tailored assistance. IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP. If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023.

Over time, predictive text learns from you and the language you use to create a personal dictionary. People go to social media to communicate, be it to read and listen or to speak and be heard. As a company or brand you can learn a lot about how your customer feels by what they comment, post about or listen to. Smart search is another tool that is driven by NPL, and can be integrated to ecommerce search functions. This tool learns about customer intentions with every interaction, then offers related results. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible.

Instead of needing to use specific predefined language, a user could interact with a voice assistant like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them. Businesses in industries such as pharmaceuticals, legal, insurance, and scientific research can leverage the huge amounts of data which they have siloed, in order to overtake the competition. However, there is still a lot of work to be done to improve the coverage of the world’s languages. Facebook estimates that more than 20% of the world’s population is still not currently covered by commercial translation technology. In general coverage is very good for major world languages, with some outliers (notably Yue and Wu Chinese, sometimes known as Cantonese and Shanghainese). You would think that writing a spellchecker is as simple as assembling a list of all allowed words in a language, but the problem is far more complex than that.

MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results. It can sort through large amounts of unstructured data to give you insights within seconds. Natural language processing, or NLP, is a field of AI that enables computers to understand language like humans do. Our eyes and ears are equivalent to the computer’s reading programs and microphones, our brain to the computer’s processing program. NLP programs lay the foundation for the AI-powered chatbots common today and work in tandem with many other AI technologies to power the modern enterprise. Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written — referred to as natural language.

NLP (Natural Language Processing) is an artificial intelligence technique that lets machines process and understand language like humans do using computational linguistics combined with machine learning, deep learning and statistical modeling. Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text. Sentiment analysis has been used in finance to identify emerging trends which can indicate profitable trades.

It helps machines process and understand the human language so that they can automatically perform repetitive tasks. Examples include machine translation, summarization, ticket classification, and spell check. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines. In our journey through some Natural Language Processing examples, we’ve seen how NLP transforms our interactions—from search engine queries and machine translations to voice assistants and sentiment analysis. These examples illuminate the profound impact of such a technology on our digital experiences, underscoring its importance in the evolving tech landscape.

That’s great news for businesses since NLP can have a dramatic effect on how you run your day-to-day operations. It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. The field of NLP has been around for decades, but recent advances in machine learning have enabled it to become increasingly powerful and effective. Companies are now able to analyze vast amounts of customer data and extract insights from it.

By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response. NLP can also help you route the customer support tickets to the right person according to their content and topic. This way, you can save lots of valuable time by making sure that everyone in your customer service team is only receiving relevant support tickets.

Still, as we’ve seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results. There are many eCommerce websites and online retailers that leverage NLP-powered semantic search engines. They aim to understand the shopper’s intent when searching for long-tail keywords (e.g. women’s straight leg denim size 4) and improve product visibility. An NLP customer service-oriented example would be using semantic search to improve customer experience. Semantic search is a search method that understands the context of a search query and suggests appropriate responses.

At the same time, there is a growing trend towards combining natural language understanding and speech recognition to create personalized experiences for users. For example, AI-driven chatbots are being used by banks, airlines, and other businesses to provide customer service and support that is tailored to the individual. NLP uses either rule-based or machine learning approaches to understand the structure and meaning of text. It plays a role in chatbots, voice assistants, text-based scanning programs, translation applications and enterprise software that aids in business operations, increases productivity and simplifies different processes.

Words that appear more frequently in the sentence will have a higher numerical value than those that appear less often, and words like “the” or “a” that do not indicate sentiment are ignored. Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic. While text and voice are predominant, Natural Language Processing also finds applications in areas like image and video captioning, where text descriptions are generated based on visual content. Similarly, ticket classification using NLP ensures faster resolution by directing issues to the proper departments or experts in customer support. Businesses can tailor their marketing strategies by understanding user behavior, preferences, and feedback, ensuring more effective and resonant campaigns.

In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates. From translation and order processing to employee recruitment and text summarization, here are more NLP examples and applications across an array of industries. This information can be used to accurately predict what products a customer might be interested in or what items are best suited for them based on their individual preferences. These recommendations can then be presented to the customer in the form of personalized email campaigns, product pages, or other forms of communication.

Poor search function is a surefire way to boost your bounce rate, which is why self-learning search is a must for major e-commerce players. Several prominent clothing retailers, including Neiman Marcus, Forever 21 and Carhartt, incorporate BloomReach’s flagship product, BloomReach Experience (brX). The suite includes a self-learning search and optimizable browsing functions and landing pages, all of which are driven by natural language processing. Translation company Welocalize customizes Googles AutoML Translate to make sure client content isn’t lost in translation. This type of natural language processing is facilitating far wider content translation of not just text, but also video, audio, graphics and other digital assets.

MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats. Not only does this feature process text and vocal conversations, but it also translates interactions happening on digital platforms. Companies can then apply this technology to Skype, Cortana and other Microsoft applications. Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services. Deep 6 AI developed a platform that uses machine learning, NLP and AI to improve clinical trial processes.

In this piece, we’ll go into more depth on what NLP is, take you through a number of natural language processing examples, and show you how you can apply these within your business. Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach.