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)

Understanding the basics of machine learning

First of all, we will understand what machine learning is. Traditionally, programming is all about defining all the steps to reach a certain predefined outcome. During this process of programming, we define each of the minute steps using a programming language that help us achieve our outcome. To give you a basic understanding, I'll take a general example. Suppose that you want to write a program that will help you draw a face. You may first write the code that draws the left eye, then write the code that draws the right eye, then the nose, and so on. Here, you are writing the code for each facial attribute, but ML flips this approach. In ML, we define the outcome and the program learns the steps to achieve the defined output. So, instead of writing code for each facial attribute, we provide hundreds of samples of human faces to...