Natural Language Processing Examples in Government Data Deloitte Insights

Natural Language Processing Examples: 5 Ways We Interact Daily

natural language processing examples

NLP first rose to prominence as the backbone of machine translation and is considered one of the most important applications of NLP. Text extraction also has a variety of uses that can help IT and business professionals alike. Text extraction can be used to scan for specific identifying information across customer communications or support tickets, making it easier to route requests or search for select incidences. Have you ever texted someone and had autocorrect kick in to change a misspelled word before you hit send? Or been to a foreign country and used a digital language translator to help you communicate?

natural language processing examples

This technology is still evolving, but there are already many incredible ways natural language processing is used today. Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics. Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches.

Example of Natural Language Processing for Author Identification

Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action. Neha Malik is an Assistant Manager with the Deloitte Center for Government Insights. She researches on issues related to public-private partnerships and innovation at the federal, state, and local government level. Pankaj Kishnani from the Deloitte Center for Government Insights also contributed to the research of the project, while Mahesh Kelkar from the Center provided thoughtful feedback on the drafts.

  • At any time ,you can instantiate a pre-trained version of model through .from_pretrained() method.
  • Knowing what people are saying about you or your products is key to maintaining a good reputation.
  • However, NLP has reentered with the development of more sophisticated algorithms, deep learning, and vast datasets in recent years.
  • So a document with many occurrences of le and la is likely to be French, for example.
  • The first step is to define the problems the agency faces and which technologies, including NLP, might best address them.
  • This makes for fun experiments where individuals will share entire sentences made up entirely of predictive text on their phones.

In recent years digital personal assistants, such as Alexa have become increasingly common. This helps a brand to build a presence and maintain commercial awareness. Automation also enables company employees to focus on more high-value tasks. Natural language processing allows for the automation of customer communication. As this application develops, alongside other smart driving solutions NLP will be key to features such as the virtual valet.

Large volumes of textual data

Because the data is unstructured, it’s difficult to find patterns and draw meaningful conclusions. Tom and his team spend much of their day poring over paper and digital documents to detect trends, patterns, and activity that could raise red flags. Now, however, it can translate grammatically complex sentences without any problems. This is largely thanks to NLP mixed with ‘deep learning’ capability.

natural language processing examples

Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. 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. Companies nowadays have to process a lot of data and unstructured text.

Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can notified of any issues they are facing and deal with them as quickly they crop up. Search engines no longer just use keywords to help users reach their search results. They now analyze people’s intent when they search for information through NLP. Like we said earlier that getting insights into the users’ response to any product or service helps organizations to offer better solutions next time. Social media is one of the most important tools to gain what and how users are responding to a brand.


Similarly, Taigers software is designed to allow insurance companies the ability to automate claims processing systems. Natural language processing allows companies to better manage and monitor operational risks. Manual searches can be time-consuming, repetitive and prone to human error. For the financial sector NLPs ability to reduce risk and improve risk models may prove invaluable. Natural language processing can also help companies to predict and manage risk.

A subfield of NLP called natural language understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications. NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own. One of the most interesting applications of NLP is in the field of content marketing.

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This is just the beginning of how natural language processing is becoming the backbone of numerous technological advancements that influence how we work, learn, and navigate life. But it doesn’t just affect and support digital communications, it’s making an impact on the IT world. Whether you’re considering a career in IT or looking to uplevel your skill set, WGU can support your efforts—and help you learn more about NLP—in a degree program that can fit into your lifestyle. Extraction-based summarization creates a summary based on key phrases, while abstraction-based summarization creates a summary based on paraphrasing the existing content—the latter of which is used more often.

This significantly speeds up the hiring process and ensures the best fit between candidates and job requirements. Natural Language Processing is more than just a trendy term in technology; it is a catalyst for the development of several industries, and businesses from all sectors are using its potential. Let’s examine 9 real-world NLP examples that show how high technology is used in various industries.

Often overlooked or may be used too frequently, NLP has been missed or skipped on many occasions. And there are many natural language processing examples that we all are using for the last many years. Before knowing them in detail, let us first understand a few things about NLP. 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. I often work using an open source library such as Apache Tika, which is able to convert PDF documents into plain text, and then train natural language processing models on the plain text. However even after the PDF-to-text conversion, the text is often messy, with page numbers and headers mixed into the document, and formatting information lost.

The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. Natural Language Processing started in 1950 When Alan Mathison Turing published an article in the name Computing Machinery and Intelligence. It talks about automatic interpretation and generation of natural language.

This is done by using NLP to understand what the customer needs based on the language they are using. This is then combined with deep learning technology to execute the routing. 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. Through NLP, computers don’t just understand meaning, they also understand sentiment and intent. They then learn on the job, storing information and context to strengthen their future responses. In this piece, we’ll go into more depth on what NLP is, take you through a number of natural language processing examples, and show you how you can apply these within your business.

Their mobile app has an AI-powered chatbot virtual barista that accepts orders verbally or textually. After getting client confirmation, the chatbot understands the demand and transmits it to the nearby Starbucks location. Starbucks also uses natural language processing for opinion analysis to keep track of consumer comments on social media. It assesses public opinion of its goods and services and offers data that can be used to boost customer happiness and promote development. NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence.

natural language processing examples

AI-powered content marketing and SEO platforms like Scalenut help marketers create high-quality content on the back of NLP techniques like named entity recognition, semantics, syntax, and big-data analysis. 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.

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