Book Image

Python Natural Language Processing

Book Image

Python Natural Language Processing

Overview of this book

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.
Table of Contents (13 chapters)

Introduction

In this chapter, we'll have a gentle introduction tonatural language processing (NLP) and how natural language processing concepts are used in real-life artificial intelligence applications. We will focus mainly on Python programming paradigms, which are used to develop NLP applications. Later on, the chapter has a tips section for readers. If you are really interested in finding out about the comparison of various programming paradigms for NLP and why Python is the best programming paradigm then, as a reader, you should go through the Preface of this book. As an industry professional, I have tried most of the programming paradigms for NLP. I have used Java, R, and Python for NLP applications. Trust me, guys, Python is quite easy and efficient for developing applications that use NLP concepts.

We will cover following topics in this chapter:

  • Understanding NLP
  • Understanding basic applications
  • Understanding advance applications
  • Advantages of the togetherness--NLP and Python
  • Environment setup for NLTK
  • Tips for readers