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Introduction to Natural Language Processing (NLP)

What is Natural Language processing

Natural language processing (NLP) is the part of computer science and artificial intelligence (AI) that gives computers the ability to understand, manipulate and interpret human language.

Natural language processing combines the computational linguistics rule based modeling of human language with machine learning and deep learning models. These technologies help computers to process the human language in the form of audio data or text data to understand the meaning and intent and sentiment of the writers of that data.

Why is NLP important ?

Large Volume of textual data are generated

Nowadays a lot of text data is being generated through facebook and twitter and other different sources. These data are not possible to analyze manually.

Natural language processing (NLP) helps computers to communicate with human language text data in their own language and scales other languages related tasks. E. g. Natural language processing helps to read the text data, voice data and interpret it and measure the sentiment and analyze the text data.

These machines can analyze in a more language unbiased way than humans without fatigue and in a consistent way.

How does Natural language processing works

Natural language processing enables computers to understand human language just like humans understand. Natural language processing can understand textual data and voice data, it uses the artificial intelligence technique to take real world input, process it and make sense in a way that computers can understand.

There are two phase of natural language processing

  • Data preprocessing

  • Algorithm development

Data pre-processing : Data preprocessing is the process to clean the text data and make it prepare to feed it in the model. Preprocessing transforms data into usable form and highlights textual elements that an algorithm can use.

Tokenization : Tokenization is the process of splitting text data, sentence, paragraph, or text document into a small unit.

Stopwords Removal : Stopwords are the most common words in any language. These stopwords might not be important while analyzing the text data and building the NLP model.

Lemmatization and stemming : This is to replace the words by their root words.

Part of speech tagging : This is when words are classified according to their part of speech, such as nouns, verbs, and adjectives.

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