Natural Language Processing makes the semantics of human language comprehendible to the systems, devices, and machines.
Communication is the key to progress! This phrase has been regaled innumerable times, to establish a successful business. Be it an employee-employer relationship or owner-client/customer relationship, communication is the driving force behind successful decision making. Over the years, communication has been enthralled with innovations in technology. From the discovery of telephone to the integration of voice recognition technique into Amazon’s Alexa, the technology has revolutionized how humans communicate. Moreover, the key to good communication is accomplished by understanding the complexities of the language. Language is an inherent behavior by living organisms and includes semantics such as words, signs or images. A human being, while reaching adolescence becomes well-versed about understanding the different aspects of communication. But modern technology-driven devices require an immense amount of learning and training before they can understand the semantics of the language. With Artificial Intelligence technologies and Machine Learning models, this task becomes easier.
Specifically, the AI technology, Natural Language Processing, is integrated into every system of communication, to make the task feasible.
In this article, we will observe the prominent applications of Natural Language Processing (NLP) in communication.
The amount of mails anyone receives is overwhelming. While some e-mails are business-related, others are only sent for promotional purposes. With the help of natural language processing (NLP), these emails can be categorised as primary, social or promotions. Additionally, with the adaptation of NLP, spam filters can be integrated into the system depending upon the semantics of the language. This is already used with Gmail, where it makes the inbox manageable.
“Hey Siri”! This term is the trademark of Apple’s iPhone. With such an invasive voice recognition technology, this phrase is popular amongst human beings, whether they are an iPhone user or not. But have we ever paused for a moment, how Apple’s Siri, or Amazon’s Alexa, answers all our questions with precision? Or how they comprehend human language with a prompt response? The answer to these questions is the integration of Natural Language processing in Apple’s Siri and Amazon’s Alexa. With the help of NLP, the applications like Alexa and Siri picks up the contextual clues with the ML model that assists them in answering the questions with promptness and precision. Tech experts suggest that in the distant future, NLP will be the world where humans will live in.
While promoting company profiles, articles, blogs or even making a website, the emphasis is given to the search results. Also, many job applications demand search results or search engine optimization to be the priority. And while this word has created the buzz for increasing customer’s engagement and is a key to marketing, it becomes imperative to know the “What” of this application.
The “What” of this application is the integration of NLP into the system, which surfaces relevant results based on the behaviors and semantics of language it is trained on. An example of this would be Google’s Search option, which understands the query of the user based on the few words that they have typed.
Autocorrect! We use simple words numerous times in a day while using any device. We take this simple application for granted without even realizing the science behind such application. The predictive analytics of NLP is the reason behind the devices’ familiarity with autocorrect. They predict things based on the semantics they are trained in and will either finish the word or suggest a relevant one. Moreover, they allow the user to customize their language preferences and learn from them.
Now businesses do not need a Spanish Thesaurus to translate what the client is saying in Spanish into English. By integrating NLP into devices or applications, the online translators automatically translate the language accurately. An example of this would be Google’s Keyboard in mobile which makes the translation task easier.
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