NLP is used successfully today in speech pattern recognition, weather forecasting, healthcare applications, and classifying handwritten documents. There are in fact so many NLP applications in business we ourselves use daily that we don’t even realise how ubiquitous the technology really is.

1. Will the Future of Search
be Semantic in 2021?
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2. Table of Contents
● What is Natural Language Processing?
● Which Are the Major Categories of NLP Technology?
● Why is NLP so important?
● What are examples of NLP applications in business?
● What’s the difference between NLP and Text Analytics?
● Neural Networks

3. What is Natural Language
Processing?
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4. Natural Language Processing (NLP) is an artificial intelligence (AI) technology that allows a machine to recognize
and decipher the nuances of human language. It organizes unstructured data by analyzing it for relevancy,
differences in spellings, correlation, and semantic meaning. It tries to understand different lexicons, grammatical
syntaxes, and the relation between words and phrases, just as a human does. And remembers it.
NLP is used successfully today in speech pattern recognition, weather forecasting, healthcare applications, and
classifying handwritten documents. There are in fact so many NLP applications in business we ourselves use
daily that we don’t even realise how ubiquitous the technology really is. Smart assistants like Siri and Alexa, our
car navigation system that tells us the fastest route, our favourite OTT streaming channel that suggests which
movies we’d like to watch, autocomplete predictive texts on our phones, translation apps - they are all examples
of how NLP has become an integral part of our lives.

5. Which Are the Major Categories of NLP
Technology?

6. Why is NLP so important?
The interest companies are showing in embracing NLP-based
solutions is gaining momentum fast. According to an industry report,
the forecasted global NLP market size is set to be US$ 35.1 Billion
by 2026. The rise is in almost all verticals including healthcare, credit
card and insurance fraud investigations, and text analytics for
customer sentiment analysis. NLP is also generating a great deal of
interest in intelligent document analysis in aviation, drone control,
robotics, and heavy machinery industries. Companies are realizing
that AI-powered solutions are only going to get bigger and better.
And if you don’t explore the technology now, doesn’t mean your
competitors won’t.

7. What are examples of NLP applications in
business?

8. What are the challenges in managing
consumer insights data?

9. What’s the difference between NLP and
Text Analytics?
NLP technology understands, interprets, and classifies a company’s raw, unstructured big data
collected from different sources like customer reviews, social media listening, employee forums, etc.
Text analytics takes this now organized data, and drives it through machine learning (ML)
algorithms to gain insights from it. This is how text analytics helps a company discover business
intelligence for prescriptive and predictive analytics within minutes.
But before an ML model can begin work on a set of data for your industry, and you in particular, it
has to be trained. And in order to be trained, it needs to have an annotated corpus of data that is
representative of the text that will be eventually analyzed. Without NLP, there is precious little that
can be done to train the machine model.

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