10 Examples of Natural Language Processing in Action

Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. 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. Not long ago, the idea of computers capable of understanding human language seemed impossible.

Kore platform is designed to help financial institutions develop AI systems to forecast risk. Their Kore platform is designed to help financial institutions develop AI systems to forecast risk. 86% of these customers will decide not to make the purchase is they find a significant amount of negative reviews. Knowing what people are saying about you or your products is key to maintaining a good reputation. In recent years digital personal assistants, such as Alexa have become increasingly common.

Higher-level NLP applications

Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines. In the 2010s, representation learning and deep neural network-style machine learning methods became widespread in natural language processing. That popularity was due partly to a flurry of results showing that such techniques can achieve state-of-the-art results in many natural language tasks, e.g., in language modeling and parsing.

You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them. The use of voice assistants is expected to continue to grow exponentially as they are used to control home security systems, thermostats, lights, and cars – even let you know what you’re running low on in the refrigerator. The word “better” is transformed into the word “good” by a lemmatizer but is unchanged by stemming. Even though stemmers can lead to less-accurate results, they are easier to build and perform faster than lemmatizers.

Machine translation

Sifting through large documents, email chains, and employee comments can be time-consuming. Since NLP technology can infer contextual meaning, it can also succinctly summarize high volumes of language data. For example, in Workday Peakon Employee Voice, managers can view summaries of a variety of different topics. Our NLP software uses extractive summarization to select portions of text from related comments, providing managers with top-level insights sourced directly from employee feedback. As such, more companies are realizing the value of integrating NLP search capabilities into their software. Google recently launched a search engine that helps users get answers to complex questions like “How should I prepare to climb K2?

  • Watson is one of the known natural language processing examples for businesses providing companies to explore NLP and the creation of chatbots and others that can facilitate human-computer interaction.
  • It converts a large set of text into more formal representations such as first-order logic structures that are easier for the computer programs to manipulate notations of the natural language processing.
  • However, in a relatively short time ― and fueled by research and developments in linguistics, computer science, and machine learning ― NLP has become one of the most promising and fastest-growing fields within AI.
  • One of the first natural language processing examples for businesses Twiggle is known for offering advanced creations in AI, ML, and NLP on the market.
  • This is then combined with deep learning technology to execute the routing.
  • The software analyzed each article written to give a direction to the writers for bringing the highest quality to each piece.

Earlier iterations of machine translation models tended to underperform when not translating to or from English. Text classification models allow companies to tag incoming support tickets based on different criteria, like topic, sentiment, or language, and route tickets to the most suitable pool of agents. An e-commerce company, for example, might use a topic classifier to identify if a support ticket refers to a shipping problem, missing item, or return item, among other categories. 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. 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.

What is the most difficult part of natural language processing?

In that way, you can quickly identify what’s important to employees—in their own words. Arguably the best-known example of NLP, smart assistants such as Siri, Alexa and Cortana have become increasingly integrated into our lives. Using NLP, they break language down into parts of speech, word stems and other linguistic features. Then natural language understanding , which is what allows machines to understand language, and natural language generation , the part that give machines the ability to “speak”, do the rest. Watson is one of the known natural language processing examples for businesses providing companies to explore NLP and the creation of chatbots and others that can facilitate human-computer interaction.

Especially when businesses also learn that every month Facebook Messenger has 1.2 billion active users. Facebook Messenger bot is increasingly being used by businesses as a way of connecting with customers. Similar to other smart assistants, this is a voice-operated application. NLP and AI algorithms will be key to achieving this level of communication and understanding.

Virtual Assistants

Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods. Traditional Business Intelligence tools such as Power BI and Tableau allow analysts to get insights https://www.globalcloudteam.com/ out of structured databases, allowing them to see at a glance which team made the most sales in a given quarter, for example. But a lot of the data floating around companies is in an unstructured format such as PDF documents, and this is where Power BI cannot help so easily.

natural language processing examples

These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation to answer these queries. It supports Unicode characters, classifies text, multiple languages, etc. Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents. Natural Language Generation is a subfield of NLP designed to build computer systems or applications that can automatically produce all kinds of texts in natural language by using a semantic representation as input. Some of the applications of NLG are question answering and text summarization.

Deeper Insights

With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content. It can do this either by extracting the information and then creating a summary or it can use deep learning techniques to extract the information, paraphrase it and produce a unique version of the original content. Automatic summarization is a lifesaver in scientific research papers, aerospace and missile maintenance works, and other high-efficiency dependent industries that are also high-risk. NLP can be used to great effect in a variety of business operations and processes to make them more efficient.

natural language processing examples

For example, social media site Twitter is often deluged with posts discussing TV programs. By monitoring, customer response businesses are able to respond to problems and maintain a good reputation. natural language processing in action A BrightLocal survey revealed that 92% of customers read online reviews before making a purchase. Natural language processing allows for the automation of customer communication.

Make Every Voice Heard with Natural Language Processing

Chatbots reduce customer waiting times by providing immediate responses and especially excel at handling routine queries , allowing agents to focus on solving more complex issues. In fact, chatbots can solve up to 80% of routine customer support tickets. Natural language processing tools can help machines learn to sort and route information with little to no human interaction – quickly, efficiently, accurately, and around the clock. In this guide, you’ll learn about the basics of Natural Language Processing and some of its challenges, and discover the most popular NLP applications in business. Finally, you’ll see for yourself just how easy it is to get started with code-free natural language processing tools. Your personal data scientist Imagine pushing a button on your desk and asking for the latest sales forecasts the same way you might ask Siri for the weather forecast.