Most Popular Applications of Natural Language Processing

Natural Language Processing NLP Tutorial

natural language processing examples

NLP capabilities have the potential to be used across a wide spectrum of government domains. In this chapter, we explore several examples that exemplify the possibilities in this area. Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action.

natural language processing examples

Meanwhile, Health Fidelity is providing natural language processing software to identify cases of fraud in the healthcare sector. Speeding up claims processing, with the use of natural language processing, helps customer claims to be resolved more quickly. NLP allows for named entity recognition, as well as relation detection to take place in real-time with near-perfect accuracy. This is commonly done by searching for named entity recognition and relation detection. Using NLP driver text analytics to monitor viewer reaction on social media helps a production company to see how storylines and characters are being received. Natural language processing tools such as the Wonderboard by Wonderflow gather and analyse customer feedback.


Plus, we help our clients tap into an ecosystem of vendors and other

collaborators in the industry, giving them access to leading technology,

solutions, and talent that would be difficult to find otherwise. Request your free demo today to see how you can streamline your business with natural language processing and MonkeyLearn. Online translators are now powerful tools thanks to Natural Language Processing.

natural language processing examples

NLP and machine learning has been key to this evolution happening so quickly. As this information often comes in the form of unstructured data it can be difficult to access. This allows algorithms to understand and sort data found in customer feedback forms. If they are not followed natural language processing systems will struggle to understand the document and may fail. NLP powered machine translation helps us to access accurate and reliable translations of foreign texts.

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Internal data breaches account for over 75% of all security breach incidents. All you have to do is type or speak about the issue you are facing, and these NLP chatbots will generate reports, request an address change, or request doorstep services on your behalf. They use this chatbot to screen more than 1 million applications every year.

natural language processing examples

This requires an application to be intelligent enough to separate paragraphs or walls of text into appropriate sentence units. At one-time sentence boundary disambiguation was difficult to achieve. It can be seen in a number of common, every day tools such as Alexa or Siri. We will also see how it is already impacting and improving a number of industries from financial services, healthcare, self-driving cars, and many more. This application is helping to power a number of useful, and increasingly common technologies.

Natural language processing (NLP) assists the Livox application to become a communication device for individuals with disabilities. After acquiring the information, it can leverage what it understood to come up with decisions or execute an action based on the algorithms. Natural language processing (NLP) can help in extracting and synthesizing information from an array of text sources, including user manuals, news reports, and more. Gartner forecasts that 85% of all customer interactions will be managed without any human involvement by 2020.

  • Like we said earlier that getting insights into the users’ response to any product or service helps organizations to offer better solutions next time.
  • MarketMuse is one such natural language processing example powered by NLP and AI.
  • Natural language processing is also helpful in analysing large data streams, quickly and efficiently.
  • NLP has been used by IBM Watson, a top AI platform, to enhance healthcare results.
  • Stemming is used to normalize words into its base form or root form.
  • Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

Like stemming, lemmatizing reduces words to their core meaning, but it will give you a complete English word that makes sense on its own instead of just a fragment of a word like ‘discoveri’. Some sources also include the category articles (like “a” or “the”) in the list of parts of speech, but other sources consider them to be adjectives. Fortunately, you have some other ways to reduce words to their core meaning, such as lemmatizing, which you’ll see later in this tutorial.

With more and more consumer data being collected for market research, it is more important than ever for businesses to keep their data safe. With NLP-powered customer support chatbots, organizations have more bandwidth to focus on future product development. For instance, in the “tree-house” example above, Google tries to sort through all the “tree-house” related content on the internet and produce a relevant answer right there on the search results page. NLP-based text analysis can help you leverage every “bit” of data your organization collects and derive insights and information as and when required. Too many results of little relevance is almost as unhelpful as no results at all.

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Written by ODEY ALFRED

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