12 Applications of Natural Language Processing

Compared to chatbots, smart assistants in their current form are more task- and command-oriented. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. Discover how AI technologies like NLP can help you scale your online business with the right choice of words and adopt NLP applications in real life.

We also have Gmail’s Smart Compose which finishes your sentences for you as you type. NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages.

The evolution of NLP

For example, NPS surveys are often used to measure customer satisfaction. There are more than 6,500 languages in the world, all of them with their own syntactic and semantic rules. All this business data contains a wealth of valuable insights, and NLP can quickly help businesses discover what those insights are.

• This makes the digital world easier to navigate for disabled individuals of all kinds.
• Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks.
• The language with the most stopwords in the unknown text is identified as the language.
• Facebook estimates that more than 20% of the world’s population is still not currently covered by commercial translation technology.
• Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively.

Anyone who has ever misread the tone of a text or email knows how challenging it can be to translate sarcasm, irony, or other nuances of communication that are easily picked up on in face-to-face conversation. Personalized marketing is one possible use for natural language processing examples. Companies that use natural language processing customize marketing messages depending on the client’s preferences, actions, and emotions, increasing engagement rates. Additionally, that technology has the potential to produce even more virtual assistants that can comprehend complicated questions, sarcasm, and emotions, dramatically improving the user experience. This information can assist farmers and businesses in making informed decisions related to crop management and sales. It uses NLP for sentiment analysis to understand customer feedback from reviews, social media, and surveys.

NLP in agriculture: AgriTech

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. NLP sentiment analysis helps marketers understand the most popular topics around their products and services and create effective strategies. One of the biggest proponents of NLP and its applications in our lives is its use in search engine algorithms. Google uses natural language processing (NLP) to understand common spelling mistakes and give relevant search results, even if the spellings are wrong. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response.

Text classification allows companies to automatically tag incoming customer support tickets according to their topic, language, sentiment, or urgency. Then, based on these tags, they can instantly route tickets to the most appropriate pool of agents. Natural language processing and powerful machine learning algorithms (often multiple used in collaboration) are improving, and bringing order to the chaos of human language, right down to concepts like sarcasm. We are also starting to see new trends in NLP, so we can expect NLP to revolutionize the way humans and technology collaborate in the near future and beyond. Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.

Text and speech processing

Text summarizers are very helpful to content marketing teams for several reasons. Text summarizations can be used to generate social media posts for blogs as well as text for newsletters. Marketers can also use it to tag content with important keywords and fill in other metadata that make content more visible to search engines.

They accomplish things that human customer service representatives cannot, like handling incredible inquiries, operating continuously, and guaranteeing quick responses. These chatbots interact with consumers more organically and intuitively because computer learning helps them comprehend and interpret human language. Customer satisfaction and loyalty are dramatically increased by streamlining customer interactions.

Natural Language Processing

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