Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
Book Image

Hands-On Python Natural Language Processing

By: Aman Kedia, Mayank Rasu

Overview of this book

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
Table of Contents (16 chapters)
1
Section 1: Introduction
4
Section 2: Natural Language Representation and Mathematics
9
Section 3: NLP and Learning

Exploring the biology behind neural networks

Neural networks were based on the functioning of neurons in the brain. Dendrites in the brain receive input signals from the neighboring neurons. Each dendrite has a weight associated with it and the signal coming in from a specific dendrite gets multiplied by its corresponding weight. These incoming signals are then summed up in the cell body. As this summed-up value reaches a particular threshold, the summed-up signal is then sent across through the neuron's axon and is further propagated forward. The weights associated with a dendrite dictate the importance of the signal coming in through a particular dendrite. These values get changed dynamically. ANNs build upon the same context. Let's look at the structure of a basic ANN in the next section.

Neurons

An ANN is an interconnected network of neurons. Each neuron, as shown in the diagram at the end of this section, receives n input signals that are nothing but a set of features,...