Feature Extraction and Embeddings in NLP: A Beginners guide to understand Natural Language Processing

This article was published as a part of the Data Science Blogathon

Introduction

In Natural Language Processing, Feature Extraction is one of the trivial steps to be followed for a better understanding of the context of what we are dealing with. After the initial text is cleaned and normalized, we need to transform it into their features to be used for modeling. We use some particular method to assign weights to particular words within our document before modeling them. We go for numerical representation for individual words as it’s easy for the computer to process numbers, in such cases, we go for word embeddings.

Source: https://www.analyticsvidhya.com/blog/2020/06/nlp-project-information-extraction/

In this article, we will discuss the various methods of feature extraction and word embeddings practiced in Natural Language processing.

Feature Extraction:

Bag of Words:

In this method, we take each document as a collection or bag having all the words in it. The idea is to analyze the

 

 

 

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