8 Real-World Examples of Natural Language Processing NLP

Learn how to extract and classify text from unstructured data with MonkeyLearn’s no-code, low-code text analysis tools. With natural language processing and machine learning working behind the scenes, all you need to focus on is using the tools and helping them to improve their natural language understanding. 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.

• These can sometimes overwhelm human resources in converting it to data, analyzing it and then inferring meaning from it.
• With glossary and phrase rules, companies are able to customize this AI-based tool to fit the market and context they’re targeting.
• Comprehension must precede production for true internal learning to be done.
• Once professionals have adopted Covera Health’s platform, it can quickly scan images without skipping over important details and abnormalities.
• You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity.

Chatbots can effectively help users navigate to support articles, order products and services, or even manage their accounts. Given that communication with the customer is the foundation upon which most companies thrive, communicating effectively and efficiently is critical. Regardless of whether it is a traditional, physical brick-and-mortar examples of natural language setup or an online, digital marketing agency, the company needs to communicate with the customer before, during and after a sale. The use of NLP, in this regard, is focused on automating the tracking, facilitating, and analysis of thousands of daily customer interactions to improve service delivery and customer satisfaction.

How Does Natural Language Processing (NLP) Work?

The first chatbot was created in 1966, thereby validating the extensive history of technological evolution of chatbots. The working mechanism in most of the NLP examples focuses on visualizing a sentence as a ‘bag-of-words’. NLP ignores the order of appearance of words in a sentence and only looks for the presence or absence of words in a sentence. The ‘bag-of-words’ algorithm involves encoding a sentence into numerical vectors suitable for sentiment analysis. For example, words that appear frequently in a sentence would have higher numerical value.

Next, the NLG system has to make sense of that data, which involves identifying patterns and building context. Spam filters are where it all started – they uncovered patterns of words or phrases that were linked to spam messages. Since then, filters have been continuously upgraded to cover more use cases. From a corporate perspective, spellcheck helps to filter out any inaccurate information in databases by removing typo variations. Thanks to NLP, you can analyse your survey responses accurately and effectively without needing to invest human resources in this process.

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