13 Natural Language Processing Examples to Know

Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. With NLP spending expected to increase in 2023, now is the time to understand how to get the greatest value for your investment. Plus, tools like MonkeyLearn’s interactive Studio dashboard (see below) then allow you to see your analysis in one place – click the link above to play with our live public demo. Search autocomplete is a good example of NLP at work in a search engine. This function predicts what you might be searching for, so you can simply click on it and save yourself the hassle of typing it out.

Receiving large amounts of support tickets from different channels (email, social media, live chat, etc), means companies need to have a strategy in place to categorize each incoming ticket. Semantic tasks analyze the structure of sentences, word interactions, and related concepts, in an attempt to discover the meaning of words, as well as understand the topic of a text. For example, let us have you have a tourism company.Every time a customer has a question, you many not have people to answer. The stop words like ‘it’,’was’,’that’,’to’…, so on do not give us much information, especially for models that look at what words are present and how many times they are repeated. Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience.

Python and the Natural Language Toolkit (NLTK)

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. Let’s look at an example of NLP in advertising to better illustrate just how powerful it can be for business. If a marketing team leveraged findings from their sentiment analysis to create more user-centered campaigns, they could filter positive customer opinions to know which advantages are worth focussing on in any upcoming ad campaigns. An NLP customer service-oriented example would be using semantic search to improve customer experience.

AI-powered chatbots, for example, use NLP to interpret what users say and what they intend to do, and machine learning to automatically deliver more accurate responses by learning from past interactions. AI is an umbrella term for machines that can simulate human intelligence. AI encompasses systems that mimic cognitive capabilities, like learning from examples and solving problems. This covers a wide range of applications, from self-driving cars to predictive systems. In a nutshell, the goal of Natural Language Processing is to make human language ‒ which is complex, ambiguous, and extremely diverse ‒ easy for machines to understand.

Search Engine Results

Finally, you’ll see for yourself just how easy it is to get started with code-free natural language processing tools. 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.

“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. 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.

NLP allows automatic summarization of lengthy documents and extraction of relevant information—such as key facts or figures. This can save time and effort in tasks like research, news aggregation, and document management. Once you get the hang of these tools, you can build a customized machine learning model, which you can train with your own criteria to get more accurate results.

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. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia.

We rely on it to navigate the world around us and communicate with others. Yet until recently, we’ve had to rely on purely text-based inputs and commands to interact with technology. Now, natural language processing is changing the way we talk with machines, as well as how they answer. 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. For example, a company’s customers could be impacted negatively, Jardine said, if a model provider suddenly updated a model unexpectedly, or worse, failed to update a model to stay up with the times. Companies often choose the open source route, he said, when they’re concerned about controlling access to their data, but also when they want more control over the fine-tuning of a model for specialized purposes.

In this example, above, the results show that customers are highly satisfied with aspects like Ease of Use and Product UX (since most of these responses are from Promoters), while they’re not so happy with Product Features. Now that you’ve gained some insight into the basics of NLP and its current applications in business, you may be wondering how to put NLP into practice. Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs, that they can make us feel like we need to go back to grammar school. The word “better” is transformed into the word “good” by a lemmatizer but is unchanged by stemming.

Artificial Neural Network

While the application was in proof-of-concept last year, it has been rolling into deployment for specific units across marketing, he said. The application uses Adobe Firefly for image generation but augments that “with LLMs that we are training and tuning to become a brand brain,” Candy said. The app understands IBM’s persona guidelines, the brand’s tone of voice and campaign guidelines, and then creates derivatives of the content for sub-brands and the different countries IBM operates in, he said. An explosion of developers and start-ups are building any number of applications based on open-source LLMs, but we wanted to find examples of established companies using them for clearly useful projects. For our purposes, we defined an enterprise company as having at least 100 employees.

Autocomplete and predictive text are similar to search engines in that they predict things to say based on what you type, finishing the word or suggesting a relevant one. And autocorrect will sometimes even change words so that the overall message makes more sense. Predictive text will customize itself to your personal language quirks the longer you use it. This makes for fun experiments where individuals will share entire sentences made up entirely of predictive text on their phones.

Both are built on machine learning – the use of algorithms to teach machines how to automate tasks and learn from experience. The review of top NLP examples shows that natural language processing has become an integral part of our lives. It defines the ways in which we type inputs on smartphones and also reviews our opinions about products, services, and brands on social media. At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions.

• When we write, we often misspell or abbreviate words, or omit punctuation.
• You can always modify the arguments according to the neccesity of the problem.
• It is primarily concerned with giving computers the ability to support and manipulate human language.
• 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.

A direct word-for-word translation often doesn’t make sense, and many language translators must identify an input language as well as determine an output one. And just last week, IBM announced its new internal consulting product, Consulting Advantage, which leverages open-source LLMs driven by Llama 2. This includes “Library of Assistants,” powered by IBM’s wasonx platform, and assists IBM’s 160,000 consultants in designing complex services for clients. Also, while it can be more cumbersome initially to deploy an open-source model if you are running a model at scale, you can save money with open-source models, especially if you have access to your own infrastructure. “In the long term, I think it’s likely that open source will be more cost-effective, simply because you’re not paying for this additional cost of IP and development,” Jardine said.