Introduction to Natural Language Processing (NLP)

The Natural Language Processing Market is expected to grow from USD 10.2 billion in 2019 to USD 26.4 billion by 2024.  (research)

What is Natural Language Processing?

NLP or Natural Language Processing in Data Science is the field of study that focuses on the interactions between human language and computers. It is a field of Artificial Intelligence wherein computers derive, analyze and understand meaning from a human language in a smart and useful way.

Don’t think it’s an easy task!  

Computers are trained to understand structured data (having some routine), like tabular excel sheets for instance. But human language, text, and voice data become complex for the computer and that’s where NLP comes in.

The computers need to be trained to recognize the various aspects of speech based on previous experiences. It will eventually try and associate the meaning to the closest word and make sense of it. Common word processor operations treat text as a mere sequence of symbols.

NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence, and ultimately, sentences convey ideas. NLP Python algorithms help developers in creating software that understands human language, which is useful in many ways.

Real-World Scenario NLP Examples

1. Email Filters

Being one of the most basic applications of NLP online, it first started with identifying those words or phrases that would constitute spam. It has now evolved along with the email software itself.

The current Gmail inbox lets you categorize emails into three categories, namely, Primary, Social, or Promotions. This helps in prioritizing the important mails and helps in timely responding.

2. Voice Assistants

Smart voice assistants like Alexa, Google Home, and Siri recognize speech patterns thanks to voice recognition. They understand it using NLP and provide a response. Now we can have daily conversations with them and even ask them to switch on the lights, order groceries, or play songs.

This is the power of NLP in Data Science. Voice assistants are only going to get better as the years go by, we’ve only just scratched the surface!

3. Predictive Text

The autocorrect on our phones is a savior, isn’t it? Well, that’s NLP too.

Predictive text is similar to search engines where it predicts the word even before you finish typing it. It may even change words to make more sense. It is so smart that it also learns from you in return.

That's the true power of Natural Language Processing with Python.

Oh! So you made it till the end. If you liked the blog then you are gonna love this video.

Conclusion

These are just some of the NLP examples in the digital world, and it is going to keep growing as businesses start to adopt more and more NLP Python algorithms in their business methods.

NLP in data science will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation.

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