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Writer's pictureYamuna M

Examples of Natural Language Processing Systems

Natural Language Processing (NLP) is an area of artificial intelligence that assists computers in understanding, interpreting, and manipulating human languages such as English or Hindi in order to evaluate and deduce their meaning. NLP assists developers in organizing and structuring knowledge in order to execute tasks like translation, summarization, named entity identification, connection extraction, speech recognition, topic segmentation, and so on.


Why NLP is so important


In the world of Google and other search engines, shoppers expect to enter a phrase or even an idea into a search box and instantly see personalized recommendations that are clearly related to what they intended to find.

It is an interaction that must occur at a speed and scale that humans alone cannot keep up with. Instead, doing the right thing by customers requires machines and systems to continuously learn and develop insights about what customers are and what they want.



This is a huge lift for those selling products or providing content on the web, but natural language processing can significantly reduce the load. Businesses want to deliver every time and for every user, so NLP is a must.


NLP can be used to great effect in a variety of business activities and processes to make them more efficient. One of the best ways to understand NLP is to look at examples of natural language processing in practice.


1. Online Search engines


Search engines are arguably the most prevalent instances of natural language processing. When a user enters a search term into a search engine, the search engine employs an algorithm to search online material based not just on the terms entered but also on the searcher's intent. To put it another way, the search engine "gets" what the user is looking for.


2. Email Filters


Email filters are one of the most basic and early applications of NLP online. It started with spam filters, uncovering specific words or phrases that indicate a spam message. But like the early adaptations of NLP the filter was upgraded. One of the most prevalent, new applications of NLP is found in Gmail's email classification.



The system detects if emails fall into one of three categories (primary, social or promotions) based on their contents. For all Gmail users, it keeps your inbox manageable with important, relevant emails you want to review and respond to quickly.


3. Language Translation


One of the telltale signs of cheating on your Spanish homework is grammatically, it's confusing. Many languages ​​do not allow direct translation and have different orders for sentence structure that translation services ignore. But, they have come a long way. With NLP, online translators can translate languages ​​more accurately and provide grammatically correct results.


This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, the tools now detect the language based on the input text and translate it.


4. Predictive Text


Predictive text is considered as a further NLP application example. Autocorrect, autocomplete, predictive analysis text is a major part of unrecognizable smartphones.


Predictive analysis and autocomplete tasks such as search engines predicting content based on user search typing and then completing the search with suggested terms. Many times, autocorrect can even change the entire message to make the statement more meaningful.


5. Spell check


Spell-checking is another natural language processing example. This is another NLP-based feature that has been around for a while in word processors and other office productivity software. It is now integrated with many types of text entry, including mobile phones. Some tools can check your spelling as you type, and more basic implementations run a spell check after you're done. Some systems even provide a range of synonyms for the words you use.


6. Virtual assistants


Natural language processing techniques can be provided by the MasterCard Chatbot example. The bot fares well when it comes to comparing it to Facebook Messenger, but when compared to Uber's bot when it's a virtual assistant.



MasterCard's virtual assistant chatbot provides a 360 eagle view of a user's spending habits, as well as what benefits they can get from the card.


In this way NLP provides services to the customers and ends the organization by helping the customers with various solutions.


7. Emergency Identification


NLP techniques can also help you identify urgency in text. You can train an urgency detection model using your own criteria so it can recognize specific words and expressions that indicate gravity or dissatisfaction. This helps you prioritize the most important requests and ensure they don't get buried in a pile of unresolved tickets.


Emergency detection can help you improve response times and efficiency, which has a positive impact on customer satisfaction.



8. Chatbots


Most question and answer or customer support activity on corporate websites is now done through chatbots. For FAQs and other knowledge bases, some basic implementations are based on pre-programmed rules and automated responses. However, more advanced chatbots use natural language processing to understand input from users or consumers and generate their text or spoken output.


9. Social Media Monitoring


Social media is one of the most important tools for getting a sense of what and how consumers are responding to a brand. Hence, it is also considered as one of the best natural language processing examples.


Through social media reviews, ratings, and feedback, it becomes easier for organizations to deliver the results consumers are asking for. Incorporating NLP into systems can help monitor and respond to feedback more easily and effectively.


Use natural language processing to grow your business


AI and Machine Learning Automation in New York can help your business transform faster. When you improve a site's navigation, make products easier to use with support from chatbots, or develop services by analyzing feedback, your business grows.


NLP makes it possible to accomplish all of those tasks and then some. The right software can help you take advantage of this exciting and evolving technology. For an all-in-one solution, see how our AI-based technology is helping many organizations become more customer-centric.

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