Examples of natural language processing

Usually, we don’t think about the intricacies of our languages. It has been hypothesized that speaking, like walking, is a learned behavior that becomes second nature in development because it can be practiced so much. It is a natural way of communicating that relies on signs, symbols, and language to impart knowledge and understanding. Moreover, there are many exceptions to grammatical principles such as “K before E until after C”, which shows that the language does not adhere to a strict set of rules. Because of humans’ increasing reliance on computing systems to communicate and accomplish tasks, machine learning and artificial intelligence (AI) are gaining popularity. The unstructured volume of information, the absence of explicit rules, and the lack of real-world conditions or intent make what comes easily to people very difficult to do on computers.

Natural language processing will also improve through artificial intelligence and the development of augmented analytics (NLP). While artificial intelligence (AI) and natural language processing (NLP) may conjure up thoughts of robots of the future, NLP is already at work in many of the mundane aspects of our existence. Take a look at these few notable cases.

Email filters

One of the first and most elementary uses of natural language processing In the online world are email filters. In the beginning, there were spam filters, which looked for specific patterns of words and phrases that indicated that a message was spam. On the other hand, filtering has evolved, as it has with early iterations of natural language processing.

Gmail inbox organization is one of the newer and more widespread NLP applications. Incoming emails are automatically categorized as either Main, Social, or Promotional, depending on their contents. All Gmail users can benefit from this feature as it helps them focus on the most urgent messages at all times.

Smart assistants

Thanks to voice recognition, smart assistants like Apple’s Siri and Amazon’s Alexa can analyze user input, draw meaningful inferences, and provide helpful responses.

We’re used to the convenience of just saying “Hey Siri,” asking a question and receiving a context-appropriate answer. And we’re so used to talking to Siri or Alexa through our thermostat, light switches, car, and other devices.

As digital assistants like Alexa and Siri become more ubiquitous and indispensable in our daily lives – making mundane tasks like online shopping easy – users expect and even value intelligent responses and information about the same assistant.

As these assistants learn more about us, our interactions will become increasingly customized to our needs.

Online search helps

To help the average user locate what they need without having to be a processor of the search term, search engines use natural language processing (NLP) to return appropriate results based on comparable search habits or user intent. By looking at the whole picture and understanding what you mean rather than the exact search words, Google can guess how many searches might apply to your problem when you start typing and return more relevant results.

If you enter a flight number, Google will tell you the current status of that plane; If you type the ticker symbol, you will get the stock information; And if you write a math equation, Google will give you the answer.

These are just a few examples of the nuances you may encounter while conducting a search, thanks to natural language processing in the search’s ability to correlate confusing queries with relevant entities and return useful results.

predictive text

We take the convenience of our mobile phones for granted because of features like autocorrect, autocomplete, and predictive text.

Like search engines, autocomplete and predictive text fill in incomplete words or suggest related words based on what you’ve already typed.

Sometimes AutoCorrect will change individual words to improve sentence flow. What you teach them is not lost on them.

The more you use predictive text, the more it will adapt to your unique speech patterns. This enables fun experiments in which people send phrases made entirely of predictive text to each other.


Poor grammar indicates that you have not studied the foreign language. In the past, translation services often overlooked that many languages ​​do not lend themselves to literal translation and have a distinct order of sentence structure. But they have made great strides forward.

Using Natural Language Processing (NLP), online translators can provide more accurate and grammatically sound translations. This is a huge help when trying to strike up a conversation with someone who speaks a different language. Also, you can now use software that can translate content from a foreign language into your native language by typing the text.

phone calls

“This call may be recorded for training purposes” is a phrase everyone is familiar with, but few stop to consider its meaning. It turns out that these recordings are usually stored in a database for the NLP system to learn from and change in the future, although they can be used for training reasons if the client is upset.

Automated systems route incoming customer care calls to a human agent or chatbot programmed to provide appropriate responses to callers.

Many organizations, including major telecom suppliers, have used this NLP technique. NLP also allows computers to synthesize speech that closely resembles human speech. Appointment reminder calls, such as those from doctors’ offices or hospitals, can be programmed to dial automatically.

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