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A Brief Introduction to Natural Language Processing

How is Artificial Intelligence capable of interpreting multiple human languages and interacting effortlessly? The answer is Natural Language Processing.

Once upon a time, there was a Swiss Linguistic professor to think of language as a science. He cleverly pointed out how every word has a sound and how it changes its meaning with context. Later in 1957, a person called Naom Chomsky published a book – creating Phase Structure Grammar to imitate a human mind’s language in a computer system. This gave birth to a computer programming language being used to date – LISP (Locator/Identifier Separation Protocol).

We were stuck with the language of 0 and 1. But now, AI and humans form a collaborative intelligence. It may be Siri or Alexa, we are moving into a generation pretty updated with such terminologies. NLP is a branch of computer science that has given machines the ability to interpret human text and voice.

Dive in to get a brief introduction to NLP-

Instances shaping the future

The Natural Language Processing Market will increase at a CAGR of 19.49 %, from $ 11.02 billion in 2020 to $ 45.79 billion in 2028. Many companies are going through digital transformation to keep pace with the market competition. Here are a few use cases of NLP:

  • When it comes to application, deep learning techniques have helped NLP progress. A great example is the market sentiment of any product is predictable with the help of the audience reviews, posts, and feedback. This is further used in advertising campaigns to reap better results.
  • With around 4000+ languages, machine translation just doesn’t replace one word for another. The translation is more than just words. It is about understanding the intent, emotions, sarcasm, or dissent in a particular text. NLP-aided translators bring the world closer. Collaborative intelligence is only beneficial when we understand humans better.
  • The term “cyber wars” is quite prevalent these days. By leveraging NLP in Incident response plans, it can detect repetitive issues in a system. It helps identify the incident and gets us the root cause. With the analysis, you can come up with better response strategies. 

Processes to a better world

A toolkit called Natural Language Tool Kit(NLTK) was developed in the early 2000s. It is a platform for libraries & courses for symbolic and statistical NLP, particularly for the Python programming language. NLTK includes tokenization, parsing, classification, stemming, tagging, and semantic reasoning.

Initially, these processes were hand-coded. But with time, managing huge chunks of text and voice became difficult. The multiple exceptions in languages are the reason for periodic enhancement in the technology. The progress in these toolkits backed up a robust system letting us play around with natural language. 

Ever heard of neural networks? Humans are capable of learning as they experience it through their five senses. Machines can do the same with help of CNN(Convolutional Neural Networks) and RNN(Recurrent Neural Networks). These advancement aids NLP to make sense of raw and structured data. 


Understanding these use cases and processes about NLP unleashes the possibilities for a better future. AI and human relationships are strengthening with innovations. But with NLP, we can share common experiences helping humans trust machines. 

The time to get updated is now. Overwhelming amounts of data will soon turn out to be a mess. Instead, harnessing the power of NLP can help businesses stay ahead of the curve and get insights to step into a digitally transforming world.

For more such updates and perspectives around Digital Innovation, IoT, Data Infrastructure, AI & Cybersecurity, go to

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