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)

A brief overview of deep learning

Machine learning is a sub-branch of AI and deep learning is a sub-branch of ML. Refer to Figure 9.9:

Figure 9.9: Deep learning as a sub-branch of ML

Deep learning uses ANN that is not just one or two layers, but many layers deep, called deep neural network (DNN). When we use DNN to solve a given problem by predicting a possible result for the same problem, it is called deep learning.

Deep learning can use labeled data or unlabeled data, so we can say that deep learning can be used in supervised techniques as well as unsupervised techniques. The main idea of using deep learning is that using DNN and a humongous amount of data, we want the machines to generalize the particular tasks and provide us with a result that we think only humans can generate. Deep learning includes a bunch of techniques and algorithms that can help us solve various problems...