What is NLP: An Introductory Tutorial to Natural Language Processing

Have you ever wondered how robots such as Sophia or home assistants sound so humanlike? How do they understand you? All of this is because of the magic of Natural Language Processing or NLP. Using NLP you can make machines sound human-like and even ‘understand’ what you’re saying.

In this article on ‘What is NLP? The best introductory guide to NLP’ you will learn everything that you need to know about NLP.

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What Is NLP?

Humans communicate with each other using words and text. The way that humans convey information to each other is called Natural Language. Every day humans share a large quality of information with each other in various languages as speech or text.

However, computers cannot interpret this data, which is in natural language, as they communicate in 1s and 0s. The data produced is precious and can offer valuable insights. Hence, you need computers to be able to understand, emulate and respond intelligently to human speech. 

Natural Language Processing or NLP refers to the branch of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages.

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NLP combines the field of linguistics and computer science to decipher language structure and guidelines and to make models which can comprehend, break down and separate significant details from text and speech.                                               


Figure 1: Constituents of NLP

How to Perform NLP?

The steps to perform preprocessing of data in NLP include:

  • Segmentation:

You first need to break the entire document down into its constituent sentences. You can do this by segmenting the article along with its punctuations like full stops and commas. 

   Stemming      Figure 2: Segmentation

  • Tokenizing:

For the algorithm to understand these sentences, you need to get the words in a sentence and explain them individually to our algorithm. So, you break down your sentence into its constituent words and store them. This is called tokenizing, and each world is called a token.


Figure 3: Tokenization

  • Removing Stop Words:

You can make the learning process faster by getting rid of non-essential words, which add little meaning to our statement and are just there to make our statement sound more cohesive. Words such as was, in, is, and, the, are called stop words and can be removed.


Figure 4: Stop Words

  • Stemming:

It is the process of obtaining the Word Stem of a word. Word Stem gives new words upon adding affixes to them


Figure 5: Stemming

  • Lemmatization:

The process of obtaining the Root Stem of a word. Root Stem gives the new base form of a word that is present in the dictionary and from which the word is derived. You can also identify the base words for different words based on the tense, mood, gender,etc.


Figure 6: Lemmatization 

  • Part of Speech Tagging:

Now, you must explain the concept of nouns, verbs, articles, and other parts of speech to the machine by adding these tags to our words. This is called ‘part of’.


Figure 7: Part of Speech Tagging

  • Named Entity Tagging:

Next, introduce your machine to pop culture references and everyday names by flagging names of movies, important personalities or locations, etc that may occur in the document. You do this by classifying the words into subcategories. This helps you find any keywords in a sentence. The subcategories are person, location, monetary value, quantity, organization, movie. 

After performing the preprocessing steps, you then give your resultant data to a machine learning algorithm like Naive Bayes, etc., to create your NLP application. 

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Applications of NLP 

NLP is one of the ways that people have humanized machines and reduced the need for labor. It has led to the automation of speech-related tasks and human interaction. Some applications of NLP include :

  • Translation Tools: Tools such as Google Translate, Amazon Translate, etc. translate sentences from one language to another using NLP.
  • Chatbots: Chatbots can be found on most websites and are a way for companies to deal with common queries quickly.
  • Virtual Assistants: Virtual Assistants like Siri, Cortana, Google Home, Alexa, etc can not only talk to you but understand commands given to them.
    • Targeted Advertising: Have you ever talked about a product or service or just googled something and then started seeing ads for it? This is called targeted advertising, and it helps generate tons of revenue for sellers as they can reach niche audiences at the right time.
  • Autocorrect: Autocorrect will automatically correct any spelling mistakes you make, apart from this grammar checkers also come into the picture which helps you write flawlessly. 
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In this article titled ‘What is NLP? The best introductory guide to NLP’, you looked into the concept of NLP. Followed by common NLP techniques. You then saw the applications of NLP. 

If you are looking to learn the applications of NLP and become an expert in Artificial Intelligence, Simplilearn's AI Course would be the ideal way to go about it. A world-class bootcamp program covering everything including NLP, Machine Learning, Deep Learning with Keras and TensorFlow, and Advanced Deep Learning topics, this is an ideal program for anyone looking to make it big in the field of Artificial Intelligence and Machine Learning.

We hope this article taught you the basics of NLP and NLP data preprocessing. Do you have any doubts or questions for us? Mention them in this article's comments section, and we'll have our experts answer them for you at the earliest!

About the Author

Mayank BanoulaMayank Banoula

Mayank is a Research Analyst at Simplilearn. He is proficient in Machine learning and Artificial intelligence with python.

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