The Natural Language Processing Workshop

6. Vector Representation

Overview

This chapter introduces you to the various ways in which text can be represented in the form of vectors. You will start by learning why this is important, and the different types of vector representation. You will then perform one-hot encoding on words, using the preprocessing package provided by scikit-learn, and character-level encoding, both manually and using the powerful Keras library. After covering learned word embeddings and pre-trained embeddings, you will use Word2Vec and Doc2Vec for vector representation for Natural Language Processing (NLP) tasks, such as finding the level of similarity between multiple texts.

Introduction

The previous chapters laid a firm foundation for NLP. But now we will ...