8 Real-World Examples of Natural Language Processing NLP

Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data.

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. 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. 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. 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. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output.

NLP algorithms focus on linguistics, computer science, and data analysis to provide machine translation capabilities for real-world applications. “Question Answering (QA) is a research area that combines research from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). Actually, current search engine just do ‘document retrieval’, i.e. given some keywords it only returns the relevant ranked documents that contain these keywords. Hence QAS is designed to help people find specific answers to specific questions in restricted domain.

Natural Language Processing, or NLP, has emerged as a prominent solution for programming machines to decrypt and understand natural language. Most of the top NLP examples revolve around ensuring seamless communication between technology and people. The answers to these questions would determine the effectiveness of NLP as a tool for innovation. Data cleaning techniques are essential to getting accurate results when you analyze data for various purposes, such as customer experience insights, brand monitoring, market research, or measuring employee satisfaction. Data analysis companies provide invaluable insights for growth strategies, product improvement, and market research that businesses rely on for profitability and sustainability. 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.

Predictive text on your smartphone or email, text summaries from ChatGPT and smart assistants like Alexa are all examples of NLP-powered applications. Analyzing topics, sentiment, keywords, and intent in unstructured data can really boost your market research, shedding light on trends and business opportunities. You can also analyze data to identify customer pain points and to keep an eye on your competitors (by seeing what things are working well for them and which are not).

Everyday Examples of Natural Language Processing

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. Texting is convenient, but if you want to interact with a computer it’s often faster and easier to simply speak. That’s why smart assistants like Siri, Alexa and Google Assistant are growing increasingly popular.

Depending on sentence structure, this approach could easily lead to bad results (for example, from sarcasm). ELECTRA, short for Efficiently Learning an Encoder that Classifies Token Replacements Accurately, is a recent method used to train and develop language models. Instead of using MASK like BERT, ELECTRA efficiently reconstructs original words and performs well in various NLP tasks. When two adjacent words are used as a sequence (meaning that one word probabilistically leads to the next), the result is called a bigram in computational linguistics.

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That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence. Today, there is a wide array of applications natural language processing is responsible for. Machine translation (MT) is one of the first applications of natural language processing. Even though Facebooks’s translations have been declared superhuman, machine translation still faces the challenge of understanding context.

Autocomplete (or sentence completion) integrates NLP with specific Machine learning algorithms to predict what words or sentences will come next, in an effort to complete the meaning of the text. 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.

Data analysis

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. Deeper Insights empowers companies to ramp up productivity levels with a set of AI and natural language processing tools. The company has cultivated a powerful search engine that wields NLP techniques to conduct semantic searches, determining the meanings behind words to find documents most relevant to a query. Instead of wasting time navigating large amounts of digital text, teams can quickly locate their desired resources to produce summaries, gather insights and perform other tasks. At the same time, there is a growing trend towards combining natural language understanding and speech recognition to create personalized experiences for users.

On the other hand, data that can be extracted from the machine is nearly impossible for employees for interpreting all the data. A practical example of this NLP application is Sprout’s Suggestions by AI Assist feature. The capability enables social teams to create impactful responses and captions in seconds with AI-suggested copy and adjust response length and tone to best match the situation. To understand how, here is a breakdown of key steps involved in the process. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries.

These functionalities have the ability to learn and change based on your behavior. 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.

NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. NLP has advanced over time from the rules-based methods of the early period. The rules-based method continues to find use today, but the rules have given way to machine learning (ML) and more advanced deep learning approaches. AnswerRocket is one of the best natural language processing examples as it makes the best in class language generation possible.

Then, this parse tree is applied to pattern matching with the given grammar rule set to understand the intent of the request. The rules for the parse tree are human-generated and, therefore, limit the scope of the language that can effectively be parsed. The king of NLP is the Natural Language Toolkit (NLTK) for the Python language. It includes a hands-on starter guide to help you use the available Python application programming interfaces (APIs). In many cases, for a given component, you’ll find many algorithms to cover it. For example, the TextBlob libraryOpens a new window , written for NLTK, is an open-source extension that provides machine translation, sentiment analysis, and several other NLP services.

With this information, we can already start to glean some very basic meaning. For example, we can see that the nouns in the sentence include “London” and “capital”, so the sentence is probably talking about London. Next, we’ll look at each token and try to guess its part of speech — whether it is a noun, a verb, an adjective and so on. Knowing the role of each word in the sentence will help us start to figure out what the sentence is talking about.

An HMM is a probabilistic model that allows the prediction of a sequence of hidden variables from a set of observed variables. In the case of NLP, the observed variables are words, and the hidden variables are the probability of a given output sequence. Natural Language Processing allows your device to hear what you say, then understand the hidden meaning in your sentence, and finally act on that meaning. But the question this brings is What exactly is Natural Language Processing? Natural language processing example projects its potential from the last many years and is still evolving for more developed results. Also, NLP enables the computer to generate language which is close to the voice of a human.

Any business, be it a big brand or a brick and mortar store with inventory, both companies, and customers need to communicate before, during, and after the sale. To make things digitalize, Artificial intelligence has taken the momentum with greater human dependency on computing systems. The computing system can further communicate and perform tasks as per the requirements. Sprout Social’s Tagging feature is another prime example of how NLP enables AI marketing. Tags enable brands to manage tons of social posts and comments by filtering content. They are used to group and categorize social posts and audience messages based on workflows, business objectives and marketing strategies.

The next step is to figure out how all the words in our sentence relate to each other. We can assume that each sentence in English is a separate thought or idea. It will be a lot easier to write a program to understand a single sentence than to understand a whole paragraph. It would be great if a computer could read this text and understand that London is a city, London is located in England, London was settled by Romans and so on. But to get there, we have to first teach our computer the most basic concepts of written language and then move up from there.

• These algorithms work together with NER, NNs and knowledge graphs to provide remarkably accurate results.
• Topic clustering through NLP aids AI tools in identifying semantically similar words and contextually understanding them so they can be clustered into topics.
• We also have Gmail’s Smart Compose which finishes your sentences for you as you type.

“Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised. For example, the CallMiner platform leverages NLP and ML to provide call center agents with real-time guidance to drive better outcomes from customer conversations and improve agent performance and overall business performance. Take your omnichannel retail and eccommerce sales and customer experience to new heights with conversation analytics for deep customer insights. Capture unsolicited, in-the-moment insights from customer interactions to better manage brand experience, including changing sentiment and staying ahead of crises. Here at Thematic, we use NLP to help customers identify recurring patterns in their client feedback data.

So far, we’ve treated every word in our sentence as a separate entity. But sometimes it makes more sense to group together the words that represent a single idea or thing. We can use the information from the dependency parse tree to automatically group together words that are all talking about the same thing. The part-of-speech model was originally trained by feeding it millions of English sentences with each word’s part of speech already tagged and having it learn to replicate that behavior. In other words, it helps to predict the parts of speech for each token. The model analyzes the parts of speech to figure out what exactly the sentence is talking about.

Smart virtual assistants could also track and remember important user information, such as daily activities. Chatbots are a form of artificial intelligence that are programmed to interact with humans in such a way that they sound like humans themselves. Depending on the complexity of the chatbots, they can either just respond to specific keywords or they can even hold full conversations that make it tough to distinguish them from humans. First, they identify the meaning of the question asked and collect all the data from the user that may be required to answer the question.

Although spaCy lacks the breadth of algorithms that NLTK provides, it offers a cleaner API and simpler interface. The spaCy library also claims to be faster than NLTK in some areas; however, it lacks the language support of NLTK. You can find several NLP tools and libraries to fit your needs regardless of language and platform. This section lists some of the most popular toolkits and libraries for NLP.

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Companies are now able to analyze vast amounts of customer data and extract insights from it. This can be used for a variety of use-cases, including customer segmentation and marketing personalization. Artificial intelligence (AI) gives machines the ability to learn from experience as they take in more data and perform tasks like humans.

The process of gathering information helps organizations to gain insights into marketing campaigns along with monitoring what trends are in the market used by the customers majorly and what users are looking natural language programming examples for. This will help in enhancing the services for better customer experience. NLP uses rule-based approaches and statistical models to perform complex language-related tasks in various industry applications.

• It allows developers to build and train neural networks for tasks such as text classification, sentiment analysis, machine translation, and language modeling.
• For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results.
• See how Repustate helped GTD semantically categorize, store, and process their data.
• At the same time, NLP could offer a better and more sophisticated approach to using customer feedback surveys.

The most direct way to manipulate a computer is through code — the computer’s language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans. The effective classification of customer sentiments about products and services of a brand could help companies in modifying their marketing strategies. For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control. 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.

Marketers can benefit from natural language processing to learn more about their customers and use those insights to create more effective strategies. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data. Natural language processing can be used to improve customer experience in the form of chatbots and systems for triaging incoming sales enquiries and customer support requests. The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English.

Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information. This is a NLP practice that many companies, including large telecommunications providers have put to use. NLP also enables computer-generated language close to the voice of a human. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment.

However, it is also important to emphasize the ways in which people all over the world have been sharing knowledge and new ideas. You will notice that the concept of language plays a crucial role in communication and exchange of information. 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. And yet, although NLP sounds like a silver bullet that solves all, that isn’t the reality.

People understand language that flows the way they think, and that follows predictable paths so gets absorbed rapidly and without unnecessary effort. When you search on Google, many different NLP algorithms help you find things faster. Query understanding and document understanding build the core of Google search.

Analyze 100% of customer conversations to fight fraud, protect your brand reputation, and drive customer loyalty. Creating a perfect code frame is hard, but thematic analysis software makes the process much easier. If you’re currently collecting a lot of qualitative feedback, we’d love to help you glean actionable insights by applying NLP. Duplicate detection collates content re-published on multiple sites to display a variety of search results. We tried many vendors whose speed and accuracy were not as good as

Repustate’s.

It allows computers to understand the meaning of words and phrases, as well as the context in which they’re used. Even organizations with large budgets like national governments and global corporations are using data analysis tools, algorithms, and natural language processing. NLP is used for other types of information retrieval systems, similar to search engines. “An information retrieval system searches a collection of natural language documents with the goal of retrieving exactly the set of documents that matches a user’s question. 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.

Using the NLP system can help in aggregating the information and making sense of each feedback and then turning them into valuable insights. This will not just help users but also improve the services rendered by the company. At the same time, we all are using NLP on a daily basis without even realizing it. A quick look at the beginner’s guide to natural language processing can help. With greater potential in itself already, Artificial intelligence’s subset Natural language processing can derive meaning from human languages.

It helps machines or computers understand the meaning of words and phrases in user statements. The most prominent highlight in all the best NLP examples is the fact that machines can understand the context of the statement and emotions of the user. MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails. Turns out, these recordings may be used for training purposes, if a customer is aggrieved, but most of the time, they go into the database for an NLP system to learn from and improve in the future.

Furthermore, automated systems direct users to call to a representative or online chatbots for assistance. And this is what an NLP practice is all about used by companies including large telecommunications providers to use. Integrating NLP into the system, online translators algorithms translate languages in a more accurate manner with correct grammatical results. This will help users to communicate with others in various different languages. A few important features of chatbots include users to navigate articles, products, services, recommendations, solutions, etc.

In this post, we will explore the various applications of NLP to your business and how you can use Akkio to perform NLP tasks without any coding or data science skills. “Dialing into quantified customer feedback could allow a business to make decisions related to marketing and improving the customer experience. It could also allow a business to better know if a recent shipment came with defective products, if the product development team hit or miss the mark on a recent feature, or if the marketing team generated a winning ad or not. For example, any company that collects customer feedback in free-form as complaints, social media posts or survey results like NPS, can use NLP to find actionable insights in this data. 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. Here, one of the best NLP examples is where organizations use them to serve content in a knowledge base for customers or users.

Chatbots and virtual assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation. Natural language understanding is particularly difficult for machines when it comes to opinions, given that humans often use sarcasm and irony. Sentiment analysis, however, is able to recognize subtle nuances in emotions and opinions ‒ and determine how positive or negative they are.

For example, words that appear frequently in a sentence would have higher numerical value. 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.

Natural language processing (NLP) is a branch of Artificial Intelligence or AI, that falls under the umbrella of computer vision. The NLP practice is focused on giving computers human abilities in relation to language, like the power to understand spoken words and text. But you can also use the parsed output from spaCy as the input to more complex data extraction algorithms. There’s a python library called textacy that implements several common data extraction algorithms on top of spaCy. At this point, we already have a useful representation of our sentence.