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
Understanding the Basics of NLP

Natural Language Processing (NLP) is an interdisciplinary area of research aimed at making machines understand and process human languages. It is an evolving field, with a rapid increase in its acceptability and adoption in industry, and its growth is projected to continue. NLP-based applications are everywhere, and chances are that you already interact with an NLP-enabled application regularly (Alexa, Google Translate, chatbots, and so on). The objective of this book is to provide a hands-on learning experience and help you build NLP applications by understanding key NLP concepts. The book lays particular emphasis on Machine Learning (ML)- and Deep Learning (DL)-based applications and also delves into recent advances such as Bidirectional Encoder Representations from Transformers (BERT). We start this journey by providing a brief context of NLP and introduce you to some existing and evolving applications of NLP.

In this chapter, we'll cover the following topics:

  • Programming languages versus natural languages
  • Why should I learn NLP?
  • Current applications of NLP