13 Natural Language Processing Examples to Know

13 Natural Language Processing Examples to Know

Natural Language Processing With Python’s NLTK Package

NLP Examples

Statistical NLP uses machine learning algorithms to train NLP models. After successful training on large amounts of data, the trained model will have positive outcomes with deduction. In this article, we explore the basics of natural language processing (NLP) with code examples. We dive into the natural language toolkit (NLTK) library to present how it can be useful for natural language processing related-tasks. Afterward, we will discuss the basics of other Natural Language Processing libraries and other essential methods for NLP, along with their respective coding sample implementations in Python. With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content.

NLP Examples

The applications of this technology extend far and wide, equipping consumers with the means to navigate a landscape rife with deepfake-driven cyberbullying, misinformation, and fraudulent schemes. By providing users with clarity and confidence in discerning between genuine and manipulated content, McAfee aims to fortify online privacy, identity, and overall well-being. In an example Grobman called a “cheap fake,” it’s a legitimate video of a news broadcast. But some of the audio has been replaced with deepfake audio in order to set up a crypto scam environment.

FAQs on Natural Language Processing

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. You iterated over words_in_quote with a for loop and added all the words that weren’t stop words to filtered_list. You used .casefold() on word so you could ignore whether the letters in word were uppercase or lowercase.

NLP Examples

Users simply have to give a topic and some context about the kind of content they want, and Scalenut creates high-quality content in a few seconds. The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically tagged as one with negative sentiment. Translation applications available today use NLP and Machine Learning to accurately translate both text and voice formats for most global languages.

How To Get Started In Natural Language Processing (NLP)

With NLP-based chatbots on your website, you can better understand what your visitors are saying and adapt your website to address their pain points. Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets. As marketers, you can use NLP tools to enhance the quality of your content. By identifying NLP terms that searchers use, marketers can rank better on NLP-powered search engines and reach their target audience. A widespread example of speech recognition is the smartphone’s voice search integration.

But then it veers into a fake version of his voice casting aspersions on all the candidates and praising Donald Trump. The deepfake material in this case was used to create something crass and funny. At CES 2024, McAfee showcased the first public demonstrations of Project Mockingbird, inviting attendees to experience the groundbreaking technology firsthand.

A conversational AI (often called a chatbot) is an application that understands natural language input, either spoken or written, and performs a specified action. A conversational interface can be used for customer service, sales, or entertainment purposes. NLP can be used in chatbots and computer programs that use artificial intelligence to communicate with people through text or voice. The chatbot uses NLP to understand what the person is typing and respond appropriately.

  • This alone is a wonder of the world where robots are commanding the way humans work more than ever.
  • After the text is converted, it can be used for other NLP applications like sentiment analysis and language translation.
  • These devices use NLP to understand human speech and respond appropriately.
  • You will notice that the concept of language plays a crucial role in communication and exchange of information.
  • Models can then use this information to make accurate predictions about customer preferences.

It’s a way to combat the concerning trend of using generative AI to create convincing deepfakes. Moreover, NLP is a tool of AI that will only help the realm of technology to advance and excel in the forthcoming time. The future of NLP is expected to be brighter as more and more applications of NLP are becoming popular among the masses. With respect to its tools and techniques, NLP has grown manifold and will likely do so in the long run. Yet the background work is done by NLP that makes use of AI and interprets human language with the help of linguistics.

Applications of NLP

Therefore, the credit goes to NLP when your project is rated 10/10 in terms of grammar and the kind of language used in it! For instance, grammarly is a grammar checking tool that helps one to run through their content and rectify their grammar errors in an instant . How much time does it take you to use the Google Translator and find the meaning of a french word? Well, NLP uses the technique of Machine Translation that relies on its ability to convert the meaning of a word in one language into another.

NLP Examples

NER is the technique of identifying named entities in the text corpus and assigning them pre-defined categories such as ‘ person names’ , ‘ locations’ ,’organizations’,etc.. The one word in a sentence which is independent of others, is called as Head /Root word. All the other word are dependent on the root word, they are termed as dependents. It is clear that the tokens of this category are not significant. Below example demonstrates how to print all the NOUNS in robot_doc. It is very easy, as it is already available as an attribute of token.

Filtering Stop Words

Online search is now the primary way that people access information. Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise. Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes. As the name suggests, predictive text works by predicting what you are about to write. Over time, predictive text learns from you and the language you use to create a personal dictionary. NLP is not perfect, largely due to the ambiguity of human language.

NLP Examples

IBM equips businesses with the Watson Language Translator to quickly translate content into various languages with global audiences in mind. With glossary and phrase rules, companies are able to customize this AI-based tool to fit the market and context they’re targeting. Machine learning and natural language processing technology also enable IBM’s Watson Language Translator to convert spoken sentences into text, making communication that much easier. Organizations and potential customers can then interact through the most convenient language and format. Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few. It’s important to understand that the content produced is not based on a human-like understanding of what was written, but a prediction of the words that might come next.

Real-Life Examples of NLP

It couldn’t be trusted to translate whole sentences, let alone texts. 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. Grobman said McAfee takes raw data from a video and feeds it into a classification model, where its goal is to determine whether something is one of a set of things. McAfee has used this kind of AI for a decade where it detects malware, or to identify the content of websites, like whether a website is dangerous for its identity theft intentions.

Once detected, these mentions can be analyzed for sentiment, engagement, and other metrics. This information can then inform marketing strategies or evaluate their effectiveness. Speech recognition is an excellent example of how NLP can be used to improve the customer experience. It is a very common requirement for businesses to have IVR systems in place so that customers can interact with their products and services without having to speak to a live person. A major benefit of chatbots is that they can provide this service to consumers at all times of the day.

In the sentence above, we can see that there are two “can” words, but both of them have different meanings. The second “can” word at the end of the sentence is used to represent a container that holds food or liquid. Top word cloud generation tools can transform your insight visualizations with their creativity, and give them an edge. Repustate has helped organizations worldwide turn their data into actionable insights.

What is Natural Language Processing? An Introduction to NLP – TechTarget

What is Natural Language Processing? An Introduction to NLP.

Posted: Tue, 14 Dec 2021 22:28:35 GMT [source]

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