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)


This was our first brush with deep learning for NLP. This was very a thorough introduction to torchtext and how we can leverage it with Pytorch. We also got a very broad view of deep learning as a puzzle of only two or three broad pieces: the model, the optimizer, and the loss functions. This is true irrespective of what framework or dataset you use.

We did skimp a bit on the model architecture explanation in the interest of keeping this short. We will avoid using concepts that have not been explained here in other sections.

When we are working with modern ensembling methods, we don't always know how a particular prediction is being made. That's a black box to us, in the same sense that all deep learning model predictions are a black box.

In the next chapter, we will look at some tools and techniques that will help us look into these boxes at least a...