13 Natural Language Processing Examples to Know

With the recent focus on large language models (LLMs), AI technology in the language domain, which includes NLP, is now benefiting similarly. You may not realize it, but there are countless real-world examples of NLP techniques that impact our everyday lives. 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. However, NLP has reentered with the development of more sophisticated algorithms, deep learning, and vast datasets in recent years. Today, it powers some of the tech ecosystem’s most innovative tools and platforms. To get a glimpse of some of these datasets fueling NLP advancements, explore our curated NLP datasets on Defined.ai.

Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. People go to social media to communicate, be it to read and listen or to speak and be heard. As a company examples of natural language or brand you can learn a lot about how your customer feels by what they comment, post about or listen to. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible.

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. Controlled, processable, simplified, technical, structured, and basic are just a few examples of attributes given to constructed languages of the type to be discussed here. We will call them controlled natural languages (CNL) or simply controlled languages. Basic English, Caterpillar Fundamental English, SBVR Structured English, and Attempto Controlled English are some examples; many more will be presented herein.

Optimising Healthcare Provision with NLP

As internet users, we share and connect with people and organizations online. We produce a lot of data—a social media post here, an interaction with a website chatbot there. With NLP-based chatbots on your website, you can better understand what your visitors are saying and adapt your website to address their pain points. Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets. By identifying NLP terms that searchers use, marketers can rank better on NLP-powered search engines and reach their target audience. The results of other studies were similar but not significant (Stewart 1998).

The data presented in the previous section and in the appendix allow for different kinds of aggregations and analyses. In particular, the classes and properties of the observed languages and the timeline of their evolution are interesting. It seems that all fundamental language properties mentioned in the existing literature fall into one of these general dimensions, or can be broken down into different aspects that can be mapped to these dimensions.

We emphasized the difference between characteristics of the environments of languages on the one hand and the properties of the languages themselves on the other. Both aspects are important, but the second is more difficult to capture in a quantitative way. Nine general properties have been collected to describe the application environments of CNLs. As a novel addition to this model, we proposed the four-dimensional PENS scheme to describe inherent language properties. This scheme allows for classification of CNLs on a discrete scale on the dimensions of precision, expressiveness, naturalness, and simplicity. Together, this allows us to formally model the important properties of languages and their environments in a simple way, and to put order and structure to a previously fuzzy and disconnected field.

And it’s not just customer-facing interactions; large-scale organizations can use NLP chatbots for other purposes, such as an internal wiki for procedures or an HR chatbot for onboarding employees. Organizations in any field, such as SaaS or eCommerce, can use NLP to find consumer insights from data. If you go to your favorite search engine and start typing, almost instantly, you will see a drop-down list of suggestions. If this hasn’t happened, go ahead and search for something on Google, but only misspell one word in your search. Through this blog, we will help you understand the basics of NLP with the help of some real-world NLP application examples.

Latino sine flexione, another international auxiliary language, is no longer widely spoken. Interestingly, the Bible has been translated into more than 6,000 languages and is often the first book published in a new language. By counting the one-, two- and three-letter sequences in a text (unigrams, bigrams and trigrams), a language can be identified from a short sequence of a few sentences only. 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.

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. Natural Language Processing, commonly abbreviated as NLP, is the union of linguistics and computer science. It’s a subfield of artificial intelligence (AI) focused on enabling machines to understand, interpret, and produce human language. The following is a list of some of the most commonly researched tasks in natural language processing.

That’s why machine learning and artificial intelligence (AI) are gaining attention and momentum, with greater human dependency on computing systems to communicate and perform tasks. And as AI and augmented analytics get more sophisticated, so will Natural Language Processing (NLP). While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives. Large Language Models (LLMs) are a class of deep learning models designed to process and understand vast amounts of natural language data. This is why LLMs need to process & understand huge volumes of text data and learn patterns and relationships between words in sentences.

Taking Advantage of NLP: How Businesses Are Benefiting

For example, the words «walking» and «walked» share the root «walk.» In our example, the stemmed form of «walking» would be «walk.» He is passionate about AI and its applications in demystifying the world of content marketing and SEO for marketers. He is on a mission to bridge the content gap between organic marketing topics on the internet and help marketers get the most out of their content marketing efforts.

• Although a wide variety of CNLs have been applied to a wide variety of problem domains, virtually all of them seem to be relevant to the field of computational linguistics.
• Interslavic removes the different idiosyncrasies that make writing and communication difficult.
• NLP could help businesses with an in-depth understanding of their target markets.
• These different LLM models are trained on a large or broad corpus of text datasets, which contain hundreds of millions to billions of words.

A suite of NLP capabilities compiles data from multiple sources and refines this data to include only useful information, relying on techniques like semantic and pragmatic analyses. In addition, artificial neural networks can automate these processes by developing advanced linguistic models. Teams can then organize extensive data sets at a rapid pace and extract essential insights through NLP-driven searches. Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats.

The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models. Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products. While chat bots can’t answer every question that customers may have, businesses like them because they offer cost-effective ways to troubleshoot common problems or questions that consumers have about their products.

Every author has a characteristic fingerprint of their writing style – even if we are talking about word-processed documents and handwriting is not available. Natural language processing provides us with a set of tools to automate this kind of task. When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it. When you search on Google, many different NLP algorithms help you find things faster. Because we write them using our language, NLP is essential in making search work.

Parsing is only one part of NLU; other tasks include sentiment analysis, entity recognition, and semantic role labeling. Both of these approaches showcase the nascent autonomous capabilities of LLMs. This experimentation could lead to continuous improvement in language understanding and generation, bringing us closer to achieving artificial general intelligence (AGI). Dependency parsing reveals the grammatical relationships between words in a sentence, such as subject, object, and modifiers.

Users simply have to give a topic and some context about the kind of content they want, and Scalenut creates high-quality content in a few seconds. Similar to spelling autocorrect, Gmail uses predictive text NLP algorithms to autocomplete the words you want to type. As you can see, Google tries to directly answer our searches with relevant information right on the SERPs.

Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce ambiguity and complexity. This may be accomplished by decreasing usage of superlative or adverbial forms, or irregular verbs. This uses natural language processing to analyse customer feedback and improve customer service. Natural language processing has been around for years but is often taken for granted. Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science.

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. “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. Voice assistants like Siri and Google Assistant utilize NLP to recognize spoken words, understand their context and nuances, and produce relevant, coherent responses. As we’ve witnessed, NLP isn’t just about sophisticated algorithms or fascinating Natural Language Processing examples—it’s a business catalyst.

Natural Language Processing isn’t just a fascinating field of study—it’s a powerful tool that businesses across sectors leverage for growth, efficiency, and innovation. If you used a tool to translate it instantly, you’ve engaged with Natural Language Processing. Whenever you type a query into Google and get astonishingly relevant results, Natural Language Processing is at play. The beauty of NLP doesn’t just lie in its technical intricacies but also its real-world applications touching our lives every day. As we delve into specific Natural Language Processing examples, you’ll see firsthand the diverse and impactful ways NLP shapes our digital experiences.

Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. 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. Natural language processing allows companies to better manage and monitor operational risks.

Things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. 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.

AI-powered chatbots and virtual assistants are increasing the efficiency of professionals across departments. Chatbots and virtual assistants are made possible by advanced NLP algorithms. They give customers, employees, and business partners a new way to improve the efficiency and effectiveness of processes.

We will discuss how these different LLM models work, LLM examples, and the training process involved in creating them. By the end of this post, you should have a solid understanding of why large language models are essential building blocks of today’s AI / generative AI applications. Natural Language Processing started in 1950 When Alan Mathison Turing published an article in the name Computing Machinery and Intelligence. As the technology evolved, different approaches have come to deal with NLP tasks. It is the branch of Artificial Intelligence that gives the ability to machine understand and process human languages.

NLP Vs NLU: What’s The Difference?

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.

Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. 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. A natural language is a human language, such as English or Standard Mandarin, as opposed to a constructed language, an artificial language, a machine language, or the language of formal logic.

For example, in medicine, machines can infer a diagnosis based on previous diagnoses using IF-THEN deduction rules. NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans. In today’s hyperconnected world, our smartphones have become inseparable companions, constantly gathering and transmitting data about our whereabouts and movements. This trove of information, often referred to as mobile traffic data, holds a wealth of insights about human behaviour within cities, offering a unique perspective on urban dynamics and patterns of movement. 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. Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used.

A sentence that is syntactically correct, however, is not always semantically correct. For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense. Compared to chatbots, smart assistants in their current form are more task- and command-oriented. Too many results of little relevance is almost as unhelpful as no results at all. As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes.

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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. The information that populates an average Google search results page has been labeled—this helps make it findable by search engines. However, the text documents, reports, PDFs and intranet pages that make up enterprise content are unstructured data, and, importantly, not labeled. This makes it difficult, if not impossible, for the information to be retrieved by search.

Usually, the definitions of such languages just describe restrictions on top of a given natural language that is taken for granted. With these languages, complete texts and documents can be written in a natural style, with a natural text flow, and with natural semantics. In the case of spoken languages, complete dialogs can be produced with a natural flow and a natural combination of speech acts. 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. The saviors for students and professionals alike – autocomplete and autocorrect – are prime NLP application examples. 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.

Text analysis solutions enable machines to automatically understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket. Not only does this save customer support teams hundreds of hours,it also helps them prioritize urgent tickets. Natural language processing can be used for topic modelling, where a corpus of unstructured text can be converted to a set of topics. Key topic modelling algorithms include k-means and Latent Dirichlet Allocation. You can read more about k-means and Latent Dirichlet Allocation in my review of the 26 most important data science concepts. Traditional Business Intelligence (BI) tools such as Power BI and Tableau allow analysts to get insights out of structured databases, allowing them to see at a glance which team made the most sales in a given quarter, for example.

NLP can generate human-like text for applications—like writing articles, creating social media posts, or generating product descriptions. A number of content creation co-pilots have appeared since the release of GPT, such as Jasper.ai, that automate much of the copywriting process. NLP allows automatic summarization of lengthy documents and extraction of relevant information—such as key facts or figures.

Constructed languages (or artificial languages or planned languages) are languages that did not emerge naturally but have been consciously defined. In this broad sense, the term includes (but is not limited to) languages such as Esperanto, programming languages, and CNLs. Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. NLP customer service implementations are being valued more and more by organizations. Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post.

It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one. These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP. Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries. This way, you can set up custom tags for your inbox and every incoming email that meets the set requirements will be sent through the correct route depending on its content. These two sentences mean the exact same thing and the use of the word is identical. Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence.

To bring order to their seemingly chaotic variety, more than 40 properties of such languages and their environments have been identified (Wyner et al. 2010). Many of these properties, however, are fuzzy and do not allow for a strict categorization. For the survey to be presented in Section 4, we collect nine general and clear-cut properties and give them letter codes. As it turns out, however, these properties mainly describe the application environment of languages and not so much the languages themselves.

It can sort through large amounts of unstructured data to give you insights within seconds. You can foun additiona information about ai customer service and artificial intelligence and NLP. These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation (a subfield of NLP) to answer these queries. The theory of universal grammar proposes that all-natural languages have certain underlying rules that shape and limit the structure of the specific grammar for any given language. You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools. Google Translate even includes optical character recognition (OCR) software, which allows machines to extract text from images, read and translate it. This blog post aims to provide a comprehensive understanding of large language models, their importance, and their applications in various NLP tasks.

In contrast to CNL, they do not deal with grammatical issues, that is, how to combine the terms to write complete sentences. Many CNL approaches, especially domain-specific ones, include controlled vocabularies. Controlled natural language being such a fuzzy term, it is important to clarify its meaning, to establish a common definition, and to understand the differences in related terms. In addition, it is helpful to review previous attempts to classify and characterize CNLs.

These natural language processing examples highlight the incredible adaptability of NLP, which offers practical advantages to companies of all sizes and industries. To conclude, we can come back to the aims set out in the Introduction of this article. The first goal was to get a better theoretical understanding of the nature of controlled languages. First of all, this article shows that despite the wide variety of existing CNLs, they can be covered by a single definition. The criteria of the proposed definition include virtually all languages that have been called CNLs in the literature. We could show that these languages form a widely scattered but connected cloud in the conceptual space between natural languages on the one end and formal languages on the other.