Bridging the gap between Human language and Machine understanding

Have you ever wondered how Alexa understands your commands, how a machine could instantly tell if people love or hate a product or how Google Translate works? Welcome to the captivating world of natural language processing!

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What is NLP?

Natural Language Processing (NLP) is a branch of Artificial Intelligence that combines computer science along with the field of linguistics to give machines ability to read, understand and analyze human language. Let me give you a quick example based on it in simpler words. Imagine you have a foreign friend asking you to make them learn your language, that’s what NLP does for computers!

NLP: Core Components

NLP is in general divided into two components:

• Natural Language Understanding (NLU): Since computers only understand binary language (0 and 1), so there is need of technology that can help in understanding the meaning of texts. Here comes the role of NLU! It majorly works on text processing, analysis of sentiments and extraction of meaning.
• Natural Language Generation (NLG): It is responsible for generating Human like language after NLU done its work. It is used for replying back. One common example includes GPT.

Here are the further subparts responsible for carrying out above work:

Text Preprocessing:

Text preprocessing is basically conversion of unstructured data into structured way in order to make the text easy to understand for machines.

It consists of:

• Tokenization: Splitting the big sentences into their constituent words is Tokenization.

Tokenization in NLP: Types, Challenges, Examples, Tools

• Stop Words: The words which don’t add to the meaning of the sentence, for example, ‘is’, ’are’, ‘the’ etc. are removed from the sentence.