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)

Bread and butter – most common tasks

There are several well-known text cleaning ideas. They have all made their way into the most popular tools today such as NLTK, Stanford CoreNLP, and spaCy. I like spaCy for two main reasons:

  • It's an industry-grade NLP, unlike NLTK, which is mainly meant for teaching.
  • It has good speed-to-performance trade-off. spaCy is written in Cython, which gives it C-like performance with Python code.

spaCy is actively maintained and developed, and incorporates the best methods available for most challenges.

By the end of this section, you will be able to do the following:

  • Understand tokenization and do it manually yourself using spaCy
  • Understand why stop word removal and case standardization works, with spaCy examples
  • Differentiate between stemming and lemmatization, with spaCy lemmatization examples