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Examples of Natural Language Processing By muller

Usually, we do not ponder the complexities of our languages. It’s been hypothesized that, like walking, speaking is a learned behavior that becomes second nature in growth because it can be practiced so often. It’s a natural way of communicating that relies on signs, symbols, and language to pass on knowledge and understanding. Moreover, there are numerous exceptions to grammatical principles like “K before E unless after C,” demonstrating that language does not adhere to a rigid set of rules. Because of humans’ increasing reliance on computing systems for communication and task completion, machine learning and artificial intelligence (AI) are gaining popularity. The volume of unstructured information, the absence of explicit rules, and the lack of real-world conditions or intent make what comes readily to people extremely challenging for computers.

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

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

Gmail’s inbox organization is one of the most recent and pervasive NLP implementations. Incoming emails are automatically classified as either main, social, or promotional, depending on their contents. All Gmail users can benefit from this feature because it helps them focus on the most urgent messages at all times.
Intelligent Helpers

Thanks to voice recognition, intelligent assistants such as Apple’s Siri and Amazon’s Alexa are able to analyze user input, draw meaningful conclusions, and deliver helpful responses.

We’ve grown accustomed to the convenience of simply saying “Hey Siri,” asking a question and receiving a contextually-appropriate response. And we’re getting used to talking with Siri or Alexa through the thermostat, the light switches, the car, and other devices.

As digital assistants such as Alexa and Siri become more ubiquitous and indispensable in our daily lives—making mundane tasks like online shopping a breeze—users have come to expect and even appreciate witty responses and information about the assistant’s self.

As these helpers learn more about us, our interactions will become increasingly customized to our needs.
Helps in Online Research

To help the typical user locate what they need without needing to be a search-term wizard, search engines use natural language processing (NLP) to surface proper results based on comparable search habits or user intent. By looking at the whole picture and understanding what you mean rather than the precise search words, Google can guess how many searches may apply to your problem as you begin typing and return more relevant results.

If you key in a flight number, Google will tell you the current status of that aircraft; if you type in a ticker symbol, you’ll get stock information; and if you type in a math equation, Google will give you the answer.

These are just a few examples of the nuances you might encounter while conducting a search, thanks to natural language processing in search’s ability to link confusing queries with relevant entities and return beneficial outcomes.
Predictive Text

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

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

On occasion, auto-correct will alter individual words to improve the flow of the award. 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 allows for entertaining experiments in which people will send each other statements composed completely of predictive text.
Translation

The poor grammar indicates that you didn’t do your foreign language studies. In the past, translation services often ignored that many languages ​​don’t lend themselves to literal translation and have distinct sentence structure ordering. But they’ve made great strides forward.

Using natural language processing (NLP), online translators can provide more precise and grammatically sound translations. This is of tremendous assistance when attempting to have a conversation with someone who speaks a different language. Also, you may now use software that can translate content from a foreign language into your native tongue by typing in the text.
Phone Calls

The phrase “this call may be recorded for training purposes” is one that everyone is familiar with, but few stop to consider its meaning. It turns out that these recordings are typically stored in a database for a natural language processing (NLP) system to learn from and change in the future, though they may be used for training reasons if a client is upset.

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

Many organizations, including major telecommunications suppliers, have used this NLP technique. NLP also allows computers to synthesize speech that sounds very much like human speech. Appointment reminder calls, such as those for doctors’ offices or hospitals, can be programmed to call automatically.

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