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Natural Language Processing (NLP) in AI: Top 9 Use Cases – Latest Hacking News | #hacking | #cybersecurity | #infosec | #comptia | #pentest | #hacker


We’ve all come across Amazon’s Alexa or Apple’s Siri and asked ChatGPT to answer a question. All of these, along with dozens of other applications, rely on Natural Language Processing (NLP). In this article, we will discuss the most common applications of NLP.

What is Natural Language Processing?

Natural language processing is a branch of artificial intelligence that allows computers to understand, manipulate, and generate spoken and written language.

9 Use Cases of NLP Algorithms

Companies use NLP for a wide range of tasks. Let’s discuss the most typical ones:

Sentiment Analysis

Give an algorithm a piece of text — and it will determine whether the sentiment expressed is positive, negative, or neutral. It comes in handy for classifying reviews on various sites or identifying the signs of mental illness.

Toxicity Classification

This is a separate subtype of sentiment analysis that helps classify hostile intent. It determines whether a particular expression is an insult, a threat, an obscenity, or hate speech. This feature is especially useful for moderating comments on social media.

Machine Translation

Automate translation between different languages, translating entire documents or pages. Another vivid example is social networks, where machine translation help translate posts and comments. These options help users understand the text and improve communication between people from different countries.

Grammatical Error Correction

Automatic correction of grammatical errors has saved more than one business correspondence, presentation, or article. The technology is actively used in Grammarly and Microsoft Word. Schools and universities can also use NLP to check student essays.

Topic Modeling

Topic modelling allows the detection of common themes in texts. For instance, an algorithm could identify whether an incoming document is an invoice, a contract, a complaint, or something else. With topic modelling, specialists can more swiftly mine relevant information from large quantities of documents.

Text Generation

Text generation will be useful for creating tweets, blog posts, articles, and even computer code. It also includes autocompletion. Chatbots automate the creation of answers to user questions by querying a database or generating a conversation.

Information Retrieval

Want to find documents that most closely match your query? NLP will help with this as well. Google went even further and integrated a multimodal search model that works not only with textual data but also with graphics and videos.

Summarization

Have you ever needed to shorten the text, highlighting the most important information? NLP is essential for making this task much easier. It can be accomplished in different ways. While extractive summarization evaluates sentences from the text and selects the key ones, abstractive summarizing conveys the essence of the text by paraphrasing.

Spam Detection

Algorithms analyze the sender’s text, title, and name to reach their verdict. The use of anti-spam detectors ensures a better user experience, as people aren’t bothered by unimportant messages. Suspicious ones are sent to the appropriate folder for further scrutiny.

At S-PRO https://s-pro.io/, we create NLP solutions for various industries, such as fintech, healthcare, and renewable energy. Contact us for more details.

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