What’s the future of NLP in Data Science?

In 2011, IBM Watson computer won the popular TV quiz Jeopardy! outplaying the two greatest Jeopardy! Champions Ken Jennings and Brad Rutter.

This occasion marked the emerging boom in Natural Language Processing – a form of AI that allows machines to read text by simulating the human ability to understand languages. IBM Watson is a cognitive system that combines information retrieval and natural language processing (NLP).

NLP is transforming the way humans and machines interact.

Most of us come in contact with NLP on a daily basis through website chatbots, virtual assistants like Siri, or when using Google Translate. Industry experts predict that the demand for NLP will grow exponentially. Why?

 

At the moment, about 80% of data is unstructured and the majority of this data is available in a text form – tweets, blog posts, websites – everything on the web is a multilingual text. That makes NLP one of the most dominant domains in Data Science. 

 

Fields like healthcare, e-commerce, SaaS, political campaigning, brand marketing and many more rely on NLP for decision making. That’s why mastering NLP skills is highly important for Data Scientists.

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