6 Real-World Examples of Natural Language Processing

Millions of businesses already use NLU-based technology to analyze human input and gather actionable insights. NLU tools should be able to tag and categorize the text they encounter appropriately. In order to categorize or tag texts with humanistic dimensions such as emotion, effort, intent, motive, intensity, and more, Natural Language Understanding systems leverage both rules based and statistical machine learning approaches. Two key concepts in natural language processing are intent recognition and entity recognition.

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. In the 1950s, Georgetown and IBM presented the first NLP-based translation machine, which had the ability to translate 60 Russian sentences to English automatically. Search engines no longer just use keywords to help users reach their search results.

Data scientists need to teach NLP tools to look beyond definitions and word order, to understand context, word ambiguities, and other complex concepts connected to human language. In NLP, syntax and semantic analysis are key to understanding the grammatical structure of a text and identifying how words relate to each other in a given context. While there are many challenges in natural language processing, the benefits of NLP for businesses are huge making NLP a worthwhile investment. Natural language processing plays a vital part in technology and the way humans interact with it. Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries.

But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers’ intent from many examples — almost like how a child would learn human language. Natural language processing can be an extremely helpful tool to make businesses more efficient which will help them serve their customers better and generate more revenue. As these examples of natural language processing showed, if you’re looking for a platform to bring NLP advantages to your business, you need a solution that can understand video content analysis, semantics, and sentiment mining. In summary, Natural language processing is an exciting area of artificial intelligence development that fuels a wide range of new products such as search engines, chatbots, recommendation systems, and speech-to-text systems.

After the text is converted, it can be used for other NLP applications like sentiment analysis and language translation. They are beneficial for eCommerce store owners in that they allow customers to receive fast, on-demand responses to their inquiries. This is important, particularly for smaller companies that don’t have the resources to dedicate a full-time customer support agent. Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction. Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated.

So instead of searching for “vitamin b complex” and then adjusting filters to show results under $40, a user can type or speak “I want vitamin b complex for under $40.” And attractive, relevant results will be returned. Answering customer calls and directing them to the correct department or person is an everyday use case for NLUs. Implementing an IVR system allows businesses to handle customer queries 24/7 without hiring additional staff or paying for overtime hours. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.

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. For each word in a document, the model predicts whether that word is part of an entity mention, and if so, what kind of entity is involved. For example, in “XYZ Corp shares traded for $28 yesterday”, “XYZ Corp” is a company entity, “$28” is a currency amount, and “yesterday” is a date. The training data for entity recognition is a collection of texts, where each word is labeled with the kinds of entities the word refers to. This kind of model, which produces a label for each word in the input, is called a sequence labeling model.

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. The implementation was seamless thanks to their developer friendly API and great documentation. Whenever our team had questions, Repustate provided fast, responsive support to ensure our questions and concerns were never left hanging. Compared to chatbots, smart assistants in their current form are more task- and command-oriented.

It can be used to help customers better understand the products and services that they’re interested in, or it can be used to help businesses better understand their customers’ needs. Natural language understanding is the process of identifying the meaning of a text, and it’s becoming more and more critical in business. Natural language understanding software can help you gain a competitive advantage by providing insights into your data that you never had access to before. The main benefit of NLP is that it improves the way humans and computers communicate with each other. 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.

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. 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. 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. 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.

For training your topic classifier, you’ll need to be familiar with the data you’re analyzing, so you can define relevant categories. 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.

Which are the top 14 Common NLP Examples?

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 saviors for students and professionals alike – autocomplete and https://chat.openai.com/ 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. Features like autocorrect, autocomplete, and predictive text are so embedded in social media platforms and applications that we often forget they exist.

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. Make the most out of your untapped business and customer data with this guide to the nine best text classification examples. NLP customer service implementations are being valued more and more by organizations.

Chatbots might be the first thing you think of (we’ll get to that in more detail soon). But there are actually a number of other ways NLP can be used to automate customer service. Customer service costs businesses a great deal in both time and money, especially during growth periods. For processing large amounts of data, C++ and Java are often preferred because they can support more efficient code.

Plus, a natural language search engine can reduce shadow churn by avoiding or better directing frustrated searches. This experience increases quantitative metrics like revenue per visitor (RPV) and conversion rate, but it improves qualitative ones like customer sentiment and brand trust. When a customer knows they can visit your website and see something they like, it increases the chance they’ll return. This disconnect between what a shopper wants and what retailers’ search engines are able to return costs companies billions of dollars annually.

There are 4.95 billion internet users globally, 4.62 billion social media users, and over two thirds of the world using mobile, and all of them will likely encounter and expect NLU-based responses. Consumers are accustomed to getting a sophisticated reply to their individual, unique input – 20% of Google searches are now done by voice, for example. Without using NLU tools in your business, you’re limiting the customer experience you can provide. Natural language understanding is a field that involves the application of artificial intelligence techniques to understand human languages. Natural language understanding aims to achieve human-like communication with computers by creating a digital system that can recognize and respond appropriately to human speech. NLP can help businesses in customer experience analysis based on certain predefined topics or categories.

NLP, with the support of other AI disciplines, is working towards making these advanced analyses possible. Autocorrect can even change words based on typos so that the overall sentence’s meaning makes sense. These functionalities have the ability to learn and change based on your behavior.

Top NLP Tools to Help You Get Started

Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant. Instead of needing to use specific predefined language, a user could interact with a voice assistant natural language example like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them. If someone says, “The
other shoe fell”, there is probably no shoe and nothing falling.

Natural language generation is the process by which a computer program creates content based on human speech input. There are several benefits of natural language understanding for both humans and machines. Humans can communicate more effectively with systems that understand their language, and those machines can better respond to human needs. Companies can also use natural language understanding software in marketing campaigns by targeting specific groups of people with different messages based on what they’re already interested in. Using a natural language understanding software will allow you to see patterns in your customer’s behavior and better decide what products to offer them in the future.

Intent recognition identifies what the person speaking or writing intends to do. Identifying their objective helps the software to understand what the goal of the interaction is. In this example, the NLU technology is able to surmise that the person wants to purchase tickets, and the most likely mode of travel is by airplane. The search engine, using Natural Language Understanding, would likely respond by showing search results that offer flight ticket purchases. Natural Language Understanding seeks to intuit many of the connotations and implications that are innate in human communication such as the emotion, effort, intent, or goal behind a speaker’s statement.

Natural language includes slang and idioms, not in formal writing but common in everyday conversation. The goal of a chatbot is to minimize the amount of time people need to spend interacting with computers and maximize the amount of time they spend doing other things. For example, when a human reads a user’s question on Twitter and replies with an answer, or on a large scale, like when Google parses millions of documents to figure out what they’re about. Named Entity Recognition (NER) allows you to extract the names of people, companies, places, etc. from your data.

Natural languages are full of ambiguity, which people deal with by
using contextual clues and other information. Formal languages are
designed to be nearly or completely unambiguous, which means that any
statement has exactly one meaning, regardless of context. Then, the entities are categorized according to predefined classifications so this important information can quickly and easily be found in documents of all sizes and formats, including files, spreadsheets, web pages and social text. The use of NLP in the insurance industry allows companies to leverage text analytics and NLP for informed decision-making for critical claims and risk management processes. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes.

• Two people may read or listen to the same passage and walk away with completely different interpretations.
• One of the best ways to understand NLP is by looking at examples of natural language processing in practice.
• At the end of the day, the combined benefits equate to a higher likelihood of site visitors and end users contributing to the metrics that matter most to your ecommerce business.
• The most direct way to manipulate a computer is through code — the computer’s language.

However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them. In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence. Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume.

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity. 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. To better understand the applications of this technology for businesses, let’s look at an NLP example.

Thus making social media listening one of the most important examples of natural language processing for businesses and retailers. 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.

We and our partners process data to provide:

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. 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. As mentioned earlier, virtual assistants use natural language generation to give users their desired response.

NLP combines rule-based modeling of human language called computational linguistics, with other models such as statistical models, Machine Learning, and deep learning. 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.

Natural language processing (also known as computational linguistics) is the scientific study of language from a computational perspective, with a focus on the interactions between natural (human) languages and computers. Ideally, your NLU solution should be able to create a highly developed interdependent network of data and responses, allowing insights to automatically trigger actions. Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. This is particularly important, given the scale of unstructured text that is generated on an everyday basis.

Transfer learning makes it easy to deploy deep learning models throughout the enterprise. Your software can take a statistical sample of recorded calls and perform speech recognition after transcribing the calls to text using machine translation. The NLU-based text analysis can link specific speech patterns to negative emotions and high effort levels. Using predictive modeling algorithms, you can identify these speech patterns automatically in forthcoming calls and recommend a response from your customer service representatives as they are on the call to the customer. This reduces the cost to serve with shorter calls, and improves customer feedback. Three open source tools commonly used for natural language processing include Natural Language Toolkit (NLTK), Gensim and NLP Architect by Intel.

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. Data analysis has come a long way in interpreting survey results, although the final challenge is making sense of open-ended responses and unstructured text.

When you read a sentence in English or a statement in a formal language, you
have to figure out what the structure of the sentence is (although in a natural
language you do this subconsciously). We are very satisfied with the accuracy of Repustate’s Arabic sentiment analysis, as well as their and support which helped us to successfully deliver the requirements of our clients in the government and private sector. For many businesses, the chatbot is a primary communication channel on the company website or app. It’s a way to provide always-on customer support, especially for frequently asked questions. 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.

See how Repustate helped GTD semantically categorize, store, and process their data. 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. Research on NLP began shortly after the invention of digital computers in the 1950s, and NLP draws on both linguistics and AI. However, the major breakthroughs of the past few years have been powered by machine learning, which is a branch of AI that develops systems that learn and generalize from data.

Optical Character Recognition

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. 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.

Deep learning is a kind of machine learning that can learn very complex patterns from large datasets, which means that it is ideally suited to learning the complexities of natural language from datasets sourced from the web. The understanding by computers of the structure and meaning of all human languages, allowing developers and users to interact with computers using natural sentences and communication. Two people may read or listen to the same passage and walk away with completely different interpretations. If humans struggle to develop perfectly aligned understanding of human language due to these congenital linguistic challenges, it stands to reason that machines will struggle when encountering this unstructured data.

If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. Yes, basic tasks still remain the norm — asking a quick question, playing music, or checking the weather (pictured “Hey Siri, show me the weather in San Francisco”). And the current percentage of consumers who prefer voice search to shopping online sits at around 25%. This means your team has more time to hone their ecommerce strategy while the algorithm does the brunt of the merchandising work needed to satisfy and convert user queries. At the end of the day, the combined benefits equate to a higher likelihood of site visitors and end users contributing to the metrics that matter most to your ecommerce business.

Arabic text data is not easy to mine for insight, but
with
Repustate we have found a technology partner who is a true expert in
the
field. With NLP spending expected to increase in 2023, now is the time to understand how to get the greatest value for your investment. Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. 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. However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge.

Autocomplete and predictive 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. NLP is special in that it has the capability to make sense of these reams of unstructured information. You can foun additiona information about ai customer service and artificial intelligence and NLP. Tools like keyword extractors, Chat PG sentiment analysis, and intent classifiers, to name a few, are particularly useful. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up.

For an ecommerce use case, natural language search engines have been shown to radically improve search results and help businesses drive the KPIs that matter, especially thanks to autocorrect and synonym detection. It works by collecting vast amounts of unstructured, informal data from complex sentences — and in the case of ecommerce, search queries — and running algorithmic models to infer meaning. Natural language search isn’t based on keywords like traditional search engines, and it picks up on intent better since users are able to use connective language to form full sentences and queries. Natural language generation, NLG for short, is a natural language processing task that consists of analyzing unstructured data and using it as an input to automatically create content. A major benefit of chatbots is that they can provide this service to consumers at all times of the day. Here, NLP breaks language down into parts of speech, word stems and other linguistic features.

The voracious data and compute requirements of Deep Neural Networks would seem to severely limit their usefulness. However, transfer learning enables a trained deep neural network to be further trained to achieve a new task with much less training data and compute effort. It consists simply of first training the model on a large generic dataset (for example, Wikipedia) and then further training (“fine-tuning”) the model on a much smaller task-specific dataset that is labeled with the actual target task. Perhaps surprisingly, the fine-tuning datasets can be extremely small, maybe containing only hundreds or even tens of training examples, and fine-tuning training only requires minutes on a single CPU.

Machine Translation

Also, the structure is very
important, so it is usually not a good idea to read from top to bottom, left to
right. Instead, learn to parse the program in your head, identifying the tokens
and interpreting the structure. Little things
like spelling errors and bad punctuation, which you can get away with in
natural languages, can make a big difference in a formal language. 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.

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. Of course, Natural Language Understanding can only function well if the algorithms and machine learning that form its backbone have been adequately trained, with a significant database of information provided for it to refer to. Machine learning simplifies the extremely complex task of layering business KPIs on top of personalized search results. Join us as we go into detail about natural language search engines in ecommerce, including how and why to leverage natural language search and examples of ecommerce use cases in the wild. Natural language understanding is how a computer program can intelligently understand, interpret, and respond to human speech.

Natural Language Understanding deconstructs human speech using trained algorithms until it forms a structured ontology, or a set of concepts and categories that have established relationships with one another. This computational linguistics data model is then applied to text or speech as in the example above, first identifying key parts of the language. Natural Language Understanding is a subset area of research and development that relies on foundational elements from Natural Language Processing (NLP) systems, which map out linguistic elements and structures. Natural Language Processing focuses on the creation of systems to understand human language, whereas Natural Language Understanding seeks to establish comprehension. Does your internal search engine understand natural language queries in every language you support?

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. There is now an entire ecosystem of providers delivering pretrained deep learning models that are trained on different combinations of languages, datasets, and pretraining tasks. These pretrained models can be downloaded and fine-tuned for a wide variety of different target tasks.

A chatbot may respond to each user’s input or have a set of responses for common questions or phrases. For computers to get closer to having human-like intelligence and capabilities, they need to be able to understand the way we humans speak. 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. First, remember that formal languages are much more dense than natural
languages, so it takes longer to read them.

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. Repustate has helped organizations worldwide turn their data into actionable insights. Learn how these insights helped them increase productivity, customer loyalty, and sales revenue. 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.

(Researchers find that training even deeper models from even larger datasets have even higher performance, so currently there is a race to train bigger and bigger models from larger and larger datasets). Knowledge of that relationship and subsequent action helps to strengthen the model. Natural Language Generation is the production of human language content through software. Natural language processing is the process of turning human-readable text into computer-readable data. It’s used in everything from online search engines to chatbots that can understand our questions and give us answers based on what we’ve typed.

NLP Architect by Intel is a Python library for deep learning topologies and techniques. Another one of the crucial NLP examples for businesses is the ability to automate critical customer care processes and eliminate many manual tasks that save customer support agents’ time and allow them to focus on more pressing issues. NLP, for example, allows businesses to automatically classify incoming support queries using text classification and route them to the right department for assistance. This combination of AI in customer experience allows businesses to improve their customer service which, in turn, increases customer retention. For example, sentiment analysis training data consists of sentences together with their sentiment (for example, positive, negative, or neutral sentiment).

To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic. The model was trained on a massive dataset and has over 175 billion learning parameters. As a result, it can produce articles, poetry, news reports, and other stories convincingly enough to seem like a human writer created them. Another one of the common NLP examples is voice assistants like Siri and Cortana that are becoming increasingly popular. These assistants use natural language processing to process and analyze language and then use natural language understanding (NLU) to understand the spoken language. Finally, they use natural language generation (NLG) which gives them the ability to reply and give the user the required response.

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. On average, retailers with a semantic search bar experience a 2% cart abandonment rate, which is significantly lower than the 40% rate found on websites with a non-semantic search bar. 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. When you send out surveys, be it to customers, employees, or any other group, you need to be able to draw actionable insights from the data you get back.

They are effectively trained by their owner and, like other applications of NLP, learn from experience in order to provide better, more tailored assistance. 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. Online translators are now powerful tools thanks to Natural Language Processing.

By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way. Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis. For example, if you’re on an eCommerce website and search for a specific product description, the semantic search engine will understand your intent and show you other products that you might be looking for. Natural language processing is developing at a rapid pace and its applications are evolving every day.

When you’re analyzing data with natural language understanding software, you can find new ways to make business decisions based on the information you have. Analyzing customer feedback is essential to know what clients think about your product. NLP can help you leverage qualitative data from online surveys, product reviews, or social media posts, and get insights to improve your business. 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. 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. Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce ambiguity and complexity.