13 Natural Language Processing Examples to Know

If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. It couldn’t be trusted to translate whole sentences, let alone texts. NLP is not perfect, largely due to the ambiguity of human language. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. 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. This technology allows texters and writers alike to speed-up their writing process and correct common typos.

The earliest NLP applications were hand-coded, rules-based systems that could perform certain NLP tasks, but couldn’t easily scale to accommodate a seemingly endless stream of exceptions or the increasing volumes of text and voice data. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used. Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated. 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.

NLP Example for Sentiment Analysis

Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. A whole new world of unstructured data is now open for you to explore. Now that you’ve covered the basics of text analytics tasks, you can get out there are find some texts to analyze and see what you can learn about the texts themselves as well as the people who wrote them and the topics they’re about. Named entities are noun phrases that refer to specific locations, people, organizations, and so on. With named entity recognition, you can find the named entities in your texts and also determine what kind of named entity they are.

From enhancing customer experiences with chatbots to data mining and personalized marketing campaigns, NLP offers a plethora of advantages to businesses across various sectors. Think about the last time your messaging app suggested the next word or auto-corrected a typo. This is NLP in action, continuously learning from your typing habits to make real-time predictions and enhance your typing experience. Voice assistants like Siri or Google Assistant are prime Natural Language Processing examples. They’re not just recognizing the words you say; they’re understanding the context, intent, and nuances, offering helpful responses. The use of NLP, particularly on a large scale, also has attendant privacy issues.

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Chatbots were the earliest examples of virtual assistants prepared for solving customer queries and service requests. The first chatbot was created in 1966, thereby validating the extensive history of technological evolution of chatbots. SpaCy is an open-source natural language processing Python library designed to be fast and production-ready. Indeed, programmers used punch cards to communicate with the first computers 70 years ago.

• In the following example, we will extract a noun phrase from the text.
• Concepts in an NLP are examples (samples) of generic human concepts.
• Hence, frequency analysis of token is an important method in text processing.
• So, ‘I’ and ‘not’ can be important parts of a sentence, but it depends on what you’re trying to learn from that sentence.
• However, large amounts of information are often impossible to analyze manually.

Now that you have learnt about various NLP techniques ,it’s time to implement them. There are examples of NLP being used everywhere around you , like chatbots you use in a website, news-summaries you need online, positive and neative movie reviews and so on. Hence, frequency analysis of token is an important natural language examples method in text processing. Each area is driven by huge amounts of data, and the more that’s available, the better the results. Bringing structure to highly unstructured data is another hallmark. Similarly, each can be used to provide insights, highlight patterns, and identify trends, both current and future.