What is Natural Language Processing? And it’s applications

Natural language processing (NLP) is a process of artificial intelligence that uses computers to interpret text and spoken words in the same manner that humans can. NLP uses computational linguistics, human language modeling, machine learning, and deep learning models.

These technologies, when used together, allow computers to interpret human language as text or speech data and ‘understand’ its full meaning, including the speaker or writer’s purpose and mood. NLP is used by computer programmes to translate text from one language to another and vice versa, respond to spoken commands and quickly summarize enormous amounts of information—even in real-time.

Some basic examples of Natural Language Processing is voice-activated GPS systems, digital assistants, speech-to-text dictation software, customer care chatbots, and other consumer conveniences. However, NLP is increasingly being used in corporate solutions to help businesses streamline operations, boost employee productivity, and simplify mission-critical business procedures.

Since human language is riddled with ambiguities, writing software that properly identifies the intended meaning of text or speech input is extremely challenging. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, and sentence structure variations are just a few of the irregularities in human language that take humans months to learn but that programmers teach NLP applications to recognize correctly from the start.

In many current real-world applications, natural language processing is the driving force behind machine intelligence. Listed below are a few examples:

NLP will undoubtedly gain in popularity as the amount of data continues to rise, and algorithms get more complex and accurate. Human-machine interaction is changing because of it.

The aforementioned applications of NLP show it is a technology that significantly enhances our quality of life. Unstructured information makes up as much as 80% of what we encounter. As a result, NLP is one of the most important disciplines of data science. Organizing this data is a significant problem that many academics face daily.

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