Part 5: Step by Step Guide to Master NLP – Word Embedding and Text Vectorization

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

Introduction

This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to the previous part of this article series, we almost completed the necessary steps involved in text cleaning and normalization pre-processing. After that, we will convert the processed text to numeric feature vectors so that we can feed it to computers for Machine Learning applications.

NOTE: Some concepts included in the pipeline of text preprocessing aren’t discussed yet such as Parsing, Grammar, Named Entity Recognition, etc which we will cover after complete the concepts of word embedding so that you can try to make a small project on NLP by using all the concepts covered till now in this series and increase your understanding of all the concepts.

So, In this article, we will understand the Word Embeddings with their types and discuss all the techniques covered in each of the types of word embeddings. Nowadays more recent Word Embedding approaches are used

 

 

 

To finish reading, please visit source site