13 Natural Language Processing Examples to Know

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. Too many results of little relevance is almost as unhelpful as no results at all. As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. To be useful, results must be meaningful, relevant and contextualized.

The examples of NLP use cases in everyday lives of people also draw the limelight on language translation. Natural language processing algorithms emphasize linguistics, data analysis, and computer science for providing machine translation features in real-world applications. The outline of NLP examples in real world for language translation would include references to the conventional rule-based translation and semantic translation.

Real-Life Examples of NLP

For instance, through optical character recognition (OCR), you can convert all the different types of files, such as images, PDFs, and PPTs, into editable and searchable data. It can help you sort all the unstructured data into an accessible, structured format. It is also used by various applications for predictive text analysis and autocorrect. If you have used Microsoft Word or Google Docs, you have seen how autocorrect instantly changes the spelling of words.

• The implementation was seamless thanks to their developer friendly API and great documentation.
• In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products.
• As you can see, Google tries to directly answer our searches with relevant information right on the SERPs.
• We, consider it as a simple communication, but we all know that words run much deeper than that.
• As McAfee’s tech listens to the audio, it determines where the deepfake audio starts and it can flag the fake audio.

While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.

Smart assistants

Models can then use this information to make accurate predictions about customer preferences. Companies can leverage product recommendation information through personalized product pages or email campaigns targeting specific consumer groups. It’s important to note that shoppers aren’t always looking for items that are in stock or on sale. Instead, customers want products that can meet their needs while also aligning with their values. Online retailers have realized the importance of personalized recommendations to improve their revenue stream. Most of the best NLP examples revolve around ensuring smooth communication between technology and people.

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. This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. GamesBeat’s creed when covering the game industry is “where passion meets business.” What does this mean?

In spaCy, the POS tags are present in the attribute of Token object. You can access the POS tag of particular token theough the token.pos_ attribute. Here, all words are reduced to ‘dance’ which is meaningful and just as required.It is highly preferred over stemming. The most commonly used Lemmatization technique is through WordNetLemmatizer from nltk library. Let us see an example of how to implement stemming using nltk supported PorterStemmer(). You can use is_stop to identify the stop words and remove them through below code..

Natural language processing, or NLP, is a field of AI that enables computers to understand language like humans do. Our eyes and ears are equivalent to the computer’s reading programs and microphones, our brain to the computer’s processing program. NLP programs lay the foundation for the AI-powered chatbots common today and work in tandem with many other AI technologies to power the modern enterprise. With the help of a set of algorithms, robots can communicate with humans and get things done in no time.

Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices. Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document.

In fact, if you are reading this, you have used NLP today without realizing it. Microsoft ran nearly 20 of the Bard’s plays through its Text Analytics API. The application charted emotional extremities in lines of dialogue throughout the tragedy and comedy datasets. Unfortunately, the machine reader sometimes had  trouble deciphering comic from tragic. The startup is using artificial intelligence to allow “companies to solver hard problems, faster.” Although details have not been released, Project UV predicts it will alter how engineers work.

Let’s dig deeper into natural language processing by making some examples. Hence, from the examples above, we can see that language processing is not “deterministic” (the same language has the same interpretations), and something suitable to one person might not be suitable to another. Therefore, Natural Language Processing (NLP) has a non-deterministic approach. In other words, Natural Language Processing can be used to create a new intelligent system that can understand how humans understand and interpret language in different situations.

All of us have used smart assistants like Google, Alexa, or Siri. Whether it is to play our favorite song or search for the latest facts, these smart assistants are powered by NLP code to help them understand spoken language. Sentiment analysis is a big step forward in artificial intelligence and the main reason why NLP has become so popular. By analyzing data, NLP algorithms can predict the general sentiment expressed toward a brand. The point here is that by using NLP text summarization techniques, marketers can create and publish content that matches the NLP search intent that search engines detect while providing search results.

Natural language processing can help convert text into numerical vectors and use them in machine learning models to uncover hidden insights. NLP models can analyze customer feedback and customer search history using text and voice data, as well as customer service conversations and product descriptions. Sentiment analysis is another way companies could use NLP in their operations.

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. ChatGPT is a chatbot powered by AI and natural language processing that produces unusually human-like responses.

However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge. 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.

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