14 Natural Language Processing Examples NLP Examples

Marketing is the most important practice a business commonly works upon to list them among the successful businesses. Also, without marketing, circulating the ideology of business with the globe is a bit challenging. Natural language processing techniques can be presented through the example of Mastercard chatbot. The bot was compatible when it came to comparing it with Facebook messenger but when it was more like a virtual assistant when comparing it with Uber’s bot. The technology here can perform and transform unstructured data into meaningful information. Integrating NLP into the system, online translators algorithms translate languages in a more accurate manner with correct grammatical results.

Another essential topic is sentiment analysis, which lets computers determine the sentiment underlying textual input statement is favorable, unfavorable, or neutral. This idea has broad ramifications, particularly for customer relationship management and market research. Any good, profitable company should continue to learn about customer needs, attitudes, preferences, and pain points.

Phases of Natural Language Processing

In fact, according to our 2023 CX trends guide, 88% of business leaders reported that their customers’ attitude towards AI and automation had improved over the past year. NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up.

Finally, they use natural language generation (NLG) which gives them the ability to reply and give the user the required response. Voice command activated assistants still have a long way to go before they become secure and more efficient due to their many vulnerabilities, which data scientists are working on. Till the year 1980, natural language processing systems were based on complex sets of hand-written rules. After 1980, NLP introduced machine learning algorithms for language processing. One of the key benefits of generative AI is that it makes the process of NLP bot building so much easier.

A Learning curve

Just visit the Google Translate website and select your language and the language you want to translate your sentences into. As much as 80% of an organization’s data is unstructured, and NLP gives decision-makers an option to convert that into structured data that gives actionable insights. This amazing ability of search engines to offer suggestions and save us the effort of typing in the entire thing or term on our mind is because of NLP. If you go to your favorite search engine and start typing, almost instantly, you will see a drop-down list of suggestions. Now that you have a fair understanding of NLP and how marketers can use it to enhance the effectiveness of their efforts, let’s look at some NLP examples to inspire you.

• Another area where NLP is making significant headway is in the realm of digital marketing.
• Chatbots that use NLP technology can understand your visitors better and answer questions in a matter of seconds.
• Through this blog, we will help you understand the basics of NLP with the help of some real-world NLP application examples.
• The NLP integrated features like autocomplete, autocorrection, spell checkers located in search bars can provide users a way to find & get information in a click.

One of the most striking aspects of intelligent chatbots is that with each encounter, they become smarter. Machine learning chatbots, on the other hand, are still in primary school and should be closely controlled at the beginning. NLP is prone to prejudice and inaccuracy, and it can learn to talk in an objectionable way. It is preferable to use the Twilio platform as a basic channel if you want to build NLP chatbot. Telegram, Viber, or Hangouts, on the other hand, are the best channels to use for constructing text chatbots.

The next step in the process consists of the chatbot differentiating between the intent of a user’s message and the subject/core/entity. In simple terms, you can think of the entity as the proper noun involved in the query, and intent as the primary requirement of the user. Therefore, a chatbot needs to solve for the intent of a query that is specified for the entity. Read more about the difference between rules-based chatbots and AI chatbots. And these are just some of the benefits businesses will see with an NLP chatbot on their support team. Chatbots might be the first thing you think of (we’ll get to that in more detail soon).

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