Natural Language Processing Examples in Government Data Deloitte Insights

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. 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. In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar.

A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmatically, you first need to preprocess it. In this tutorial, you’ll take your first look at the kinds of text preprocessing tasks you can do with NLTK so that you’ll be ready to apply them in future projects. You’ll also see how to do some basic text analysis and create visualizations. NLP is also a driving force behind programs designed to answer questions, often in support of customer service initiatives. Backed by AI, question answering platforms can also learn from each consumer interaction, which allows them to improve interactions over time.

Planning for NLP

There are examples of NLP in nearly every customer service process powered by AI. And companies can use sentiment analysis to understand how a particular type of user feels about a particular topic, product, etc. They can use natural language processing, computational linguistics, text analysis, etc. to understand the general sentiment of the users for their products and services and find out if the sentiment is good, bad, or neutral.

First, we must go deeper into NLP’s mechanisms to understand its significance in business. The branch of artificial intelligence, Natural Language Processing, is concerned with using natural language by computers and people to communicate. The ultimate goal of NLP is to effectively read, comprehend, and make sense of human language. By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response. 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.

Natural language processing examples

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. 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 In addition to monitoring, an NLP data system can automatically classify new documents and set up user access based on systems that have already been set up for user access and document classification. Just visit the Google Translate website and select your language and the language you want to translate your sentences into. As internet users, we share and connect with people and organizations online.

NLG is especially important in creating chatbots to answer customer questions. But it’s also used in translation tools, search functionality, and in GPS apps. It can analyze your social content for you to understand how people feel about your brand. You can use a content analyzer to create a chatbot or to determine trending topics that are worth writing about.

This is a NLP practice that many companies, including large telecommunications providers have put to use. NLP also enables computer-generated language close to the voice of a human. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

All of this adds up to a superior experience for top-tier customers, which leads to higher retention rates and more revenue. Imagine that a customer who is in a hurry calls into your contact center. Each time an agent asks the customer to hold for assistance, the customer shows growing impatience.

Introduction to Deep Learning

As shown above, the final graph has many useful words that help us understand what our sample data is about, showing how essential it is to perform data cleaning on NLP. Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. For instance, the sentence “The shop goes to the house” does not pass. In the sentence above, we can see that there are two “can” words, but both of them have different meanings.

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. Some of the most common ways NLP is used are through voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages. It’s the process of taking words and phrases that could have multiple meanings and narrowing it down to just one.

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. The results are surprisingly personal and enlightening; they’ve even been highlighted by several media outlets.

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