Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

Get well-versed with traditional as well as modern natural language processing concepts and techniques

Key Features

• Perform various NLP tasks to build linguistic applications using Python libraries
• Understand, analyze, and generate text to provide accurate results
• Interpret human language using various NLP concepts, methodologies, and tools

Book Description

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.

What you will learn

• Understand how NLP powers modern applications
• Explore key NLP techniques to build your natural language vocabulary
• Transform text data into mathematical data structures and learn how to improve text mining models
• Discover how various neural network architectures work with natural language data
• Get the hang of building sophisticated text processing models using machine learning and deep learning
• Check out state-of-the-art architectures that have revolutionized research in the NLP domain

Who this book is for

This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.

Table of Contents

1. Understanding the Basics of NLP
2. NLP Using Python
3. Building your NLP Vocabulary
4. Transforming Text into Data Structures
5. Word Embeddings and Distance Measurements for Text
6. Exploring Sentence-, Document-, and Character-Level Embeddings
7. Identifying Patterns in Text using Machine Learning
8. From Human Neurons to Artificial Neurons for Understanding Text
9. Applying Convolutions to Text
10. Capturing Temportal Relationships in Text
11. State of the Art in NLP

Aman Kedia is a data enthusiast and lifelong learner. He is an avid believer in Artificial Intelligence (AI) and the algorithms supporting it. He has worked on state-of-the-art problems in Natural Language Processing (NLP), encompassing resume matching and digital assistants, among others. He has worked at Oracle and SAP, trying to solve problems leveraging advancements in AI. He has four published research papers in the domain of AI.

Mayank Rasu has more than 12 years of global experience as a data scientist and quantitative analyst in the investment banking industry. He has worked at the intersection of finance and technology and has developed and deployed AI-based applications within the finance domain. His experience includes building sentiment analyzers, robotics, and deep learning-based document review, among many others areas.