Book Image

Natural Language Processing with Python Quick Start Guide

By : Nirant Kasliwal
Book Image

Natural Language Processing with Python Quick Start Guide

By: Nirant Kasliwal

Overview of this book

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Table of Contents (10 chapters)

Deep Learning for NLP

n the previous chapter, we used classic machine learning techniques to build our text classifiers. In this chapter, we will replace those with deep learning techniques via the use of recurrent neural networks (RNN).

In particular, we will use a relatively simple bidirectional LSTM model. If this is new to you, keep reading if not, please feel free to skip ahead!

The dataset attribute of the batch variable should point to the trn variable of the type. This is a useful checkpoint to understand how data flow differs in training deep learning models.

Let's begin by touching upon the overhyped terms, that is, deep in deep learning and neural in deep neural networks. Before we do that, let's take a moment to explain why I use PyTorch and compare it to Tensorflow and Keras—the other popular deep learning frameworks...