Understanding Natural Language Processing


Natural Language Processing is a way of converting or processing Human Spoken Language in a way the computer understands. Some of the major Applications of NLP are Text Summarization, Sentiment Analysis, Search and Name Entity Recognition. Natural Language Understanding and Natural Language Generation are challenges in NLP. The best way to perform Natural Language Processing is using Deep Learning Algorithms. Before directly jumping into Deep Learning, it’s better to start with traditional algorithms and then jump into it.

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Representing Text in Number

Starting from the basics, we have to find some way to represent words/characters in a way machine understands. And machines can only understand numbers. Hence, we have to convert our word to some number. We can create a list of words present in our data. And assign numbers to them.

For example, if I have sentences
sen1 = Ram Eats Food
sen2 = I Love Food
sen3 = Hari Eats Much

The present words are Ram, Eats, Food, I, Love, Much. Here we do not include previously used words. Our list has to be unique. Now we can label each word by number.
Ram = 0
Eats = 1
Much = 5.

In this way, we can represent any text into numbers.

Related Task: LSTM Chatbot

Data Preprocessing

But our dataset is quite large and we have to deal with noise(raw data). So, the first and foremost step of NLP is Data Preprocessing. Data Preprocessing in NLP is done in multiple steps. The Steps Involved are:

  1. Remove Stop Words [ Remove less meaningful words. Most used words like an, a, the do not have meaning alone. Symbols like ()\”@/#:<>{}. We can remove them! ]
  2. Steeming [ Reading – Read, Walks – Walk, I’ll- I will ]
  3. Lemmanitization

Now it’s time to vectorize our vocabulary. As mentioned above we can represent our text in numbers.

Traditionally, we used to use Naive Bayes for Text Classification, TfIdf to measure similarity, and so on. So, to get an in-depth idea about NLP. You have to learn from the basic and traditional algorithm.

Preparing the Model and Training

It will be very long and boring if I go on explaining all the algorithms. So, after getting your data ready, you have to create a model and train your model. You must know how a particular algorithm works, how to optimize the algorithm, and further model tuning. If you want to kick-start your career in Machine Learning especially Natural Language Processing join our AI Training Program. For more: Contact Us.


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