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 chapter covered a lot of new ground. We started by performing linguistic processing on our text. We met spaCy, which we will continue to dive deeper into as we move on in this book. We covered the following foundational ideas from linguistics, tokenization doing this with and without spaCy, stop word removal, case standardization, lemmatization (we skipped stemming) using spaCy and its peculiarities such as-PRON-

But what do we do with spaCy, other than text cleaning? Can we build something? Yes!

Not only can we extend our simple linguistics based text cleaning using spaCy pipelines but also do parts of speech tagging, named entity recognition, and other common tasks. We will look at this in the next chapter.

We looked at spelling correction or the closest word match problem. We discussed FuzzyWuzzy and Jellyfish in this context. To ensure that we can scale...