Hey Alexa, Is Natural Language Processing Your Cup Of Tea?

Natural Language Processing

If you have struggled past a lot with computer programming languages and have started wondering, why on earth won’t the dumb computer understand what you talk to it? Then it’s time to consider Natural Language Processing(NLP)!

Natural Language Processing or the more commonly abbreviated form NLP is the solution to your stressed-out question! 

Oh yes! 

Say “Hello Alexa, how’re you doing today?”

& it says 

“I am doing good. How about you?”

Cool, right?

Though NLP is not a newbie in our community nor extraordinary as it looks, it clearly needs a good introduction!

When you started the conversation addressing Alexa, 

  • The computer device got triggered, 
  • Understood that you are speaking to it, 
  • Interpreted your natural language & 
  • Humbly responded back in the similar language

That’s not it. When you further ask Alexa to play a song or ask another search question for example. It clearly does the groundwork and returns back a proper response to it within seconds. You can thank NLP for this! Of course, NLP works hand-in-hand along with Artificial Intelligence, machine learning, deep learning, and so on.  

NLP is a component of Artificial Intelligence that can be rooted back in the system for 50 years now. Surprised? Don’t be. NLP is deeply sunk in the field of linguistics and finds a plethora of real-world implementations in multiple fields including but not limited to search engines, and medical research. Let us dig more into the NLP concept and get to know what it can do?

If you relate to this and need more information on the trending topics of Data Science, Artificial Intelligence, Deep Learning, Neural Networks, & more, then visit us at our hot adda for cutting-edge technology topics!

What is NLP & why does it matter so much?

Natural Language Processing as the name goes implies the processing of Natural language. Yes, NLP is the ability of a computer to interpret the natural language that we humans speak and relate to. Here interpreting data coils up all the processes that a computer incorporates to process output to any given input, which includes- analyzing, understanding, and deriving meaning from human language in an appropriate way. Problems solved already! Before you start jumping for joy, let us tell you that it is not that simple as the above statement goes. It has plenty of strings attached to it. 

Bridging the gap between human communication and computer understanding, NLP processes the huge amount of information already available with the help of algorithms and powerful computing. 

Now, just consider a vague scenario-

When you ask Alexa, “Alexa what’s the climate like?”

Alexa literally needs to 

  1. Firstly, understand the question, 
  2. Make sense of the words, then 
  3. Start searching for information pertaining to the location & time of the day and after that
  4. Frame the answer to your query in human-understandable language. 
  5. And at the end speak it out!

All this and much more filling-the-gap processes of course, here and there, in mere seconds! Isn’t it really brilliant?

Now, if you change your mind and ask Alexa to play a song, ”Alexa, play the Titanic Song.” 

Will you be satisfied if Alexa plays some song that has the ‘TITANIC’ word in it? (say, Amy Grant song from the ‘90s, “It Takes a Little Time”) We are sure this would be awful! 

But do not worry, Alexa and all other NLP models that enter the market come in duly trained. To avoid such mistakes and confusion these NLP applications are rigorously tested and trained upon their own mistakes with the help of Machine Learning

Getting deeper into Natural Language Processing

Natural Language Processing is deeply rooted in the philosophy of linguistics. Procedures from deep learning and neural networks also share their contributions in solving NLP problems. While several practical implementations of NLP already exist, NLP still has several uncovered areas that call for our attention.

And thus it is an integral element that a Data Science professional should be well-versed with. If you wish to take up deep learning in NLP and related topics, then look no further, because you are just a click away!

As against computers, dealing with structured data, NLP often deals with unstructured data. Yes! And that’s where the complexities begin. Structured data that is organized and well understandable is a much greener side of the unstructured one that needs a lot of sorting and arrangements. 

Most importantly, 80% of business data is unstructured! Don’t agree? 

You consider any news articles, the wide bandwidth of Social media posts, emails from n number of sources, etc. They all are live models of text-based unstructured data. 

To deal with these, NLP has to study the structure and syntax of the natural language. Apart from all these, the complexities and ambiguities of Human Langauge make the study further worst. However, NLP is maturing, and we are at a better place with it already. Let us strive to unleash more in-depth NLP processes that will make our lives much better when dealing with unstructured data!

Applications of Natural Language Processing:

From Alexa to Automated Cars, Natural Language processing finds its application everywhere now. 

By employing NLP, developers can design and structure information to accomplish language processing implementations like speech recognition & summarization, language translation, named entity recognition, relationship extraction, sentiment analysis, etc.

NLP algorithms empower developers and companies to build software that understands human language. Agreed! Spoken languages are too complicated for computers to process information. 

For example, words like ate and eight

[That said can you answer: why was 6 afraid of 7?

Ha HA… That’s because 7, ate, 9!]

And ya mistakes like this can persist if the devices incorporating NLP are not adequately trained with enough data.  

NLP relies on machine learning and automatically learns the rules by analyzing large sets of examples like a collection of written texts, a book, etc.). This processed information redeems as statistical inference. 

The equation is exponential and simple => More data analyzed = More accurate model.

A few of the applications are:

  • Gmail smart reply suggestions
  • Translation Application (like Google Translate, SayHi, Microsoft Translator)
  • Personal Voice Assistants (Amazon Alexa, Google Assistant, Apple Siri, Microsoft Cortana)
  • Chatbots (Kuki Artificial Intelligence Chatbot, Meena by Google, BlenderBot by Facebook)
  • IVR Application (order entry transactions, office call routing, office call routing)
  • Predictive Text (T9, iTap, eZiText, and LetterWise/WordWise)
  • Data classification (Credit card numbers (PCI), customer personal data, privileged credentials for IT systems, protected health information (HIPAA), Social Security numbers, intellectual property, employee records, etc.)

Make your stand-out career in Data Science by mastering all the requisite skills!

What Algorithms Go in Natural Language Processing?

Natural Language Processing typically implements classification algorithms

Few algorithms that you can use for Natural Language Processing are:

  • Naive Bayes
  • Conditional Random Field.
  • Neural Networks/Deep Learning
  • Maximum entropy classifier
  • Decision Tree
  • Random Forest
  • XGBoost
  • Support Vector Machine
  • Multi-layer perceptron
  • LSTM

Why is NLP important in data science?

Let’s consider an example. You message your friend just to understand his whereabouts. And you type…

“Hey Sid, Wr r u?”

That’s a cool message and a pretty short one. No doubt we can make sense out of this. But can a computer equate “Hey Sid, Wr r u?” to “Hello Sid, Where are you?”

And it’s just the minutest data out there. Millions and millions of such data are generated every single second out there on the social media sites like Facebook, Instagram, emails, blogs, websites, and chatting apps, too! Imagine the poor computer, what would it be going through? So much confusion- structured and unstructured data!

Well, Data Science is all about DATA. And data, which is mostly in text or voice (some human understandable format) is quite a tedious workout for the computers! That said, NLP is but a very essential element in Data Science and it is the future, here for a long time! So, you can hardly find an escape but make your hands dirty with this concept of Data Science.

Wrapping Up but not doing it!!!

Well, not sure about any other blog post, but this one surely needs a part 2! So, wait until we get you another interesting read on NLP and other similar cutting-edge topics. 

Till then you can look upto our blog page for more interesting articles like this!

BTW, are you willing to take your knowledge in NLP one step further? Then do check out our Data Science Course for more detailed learning and skills.

Not sure whether Data Science is the right career for you? Need help in making a decision? Why not talk to our Expert and then take a wise call? Let us know how shall we reach out to you by filling this form.

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A traveler, and explorer, Archana is an active writer at GUVI. You can usually find her with a book/kindle binge-reading (with an affinity to the mystery/humor).

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