Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
4 (1)
Book Image

Hands-On Python Natural Language Processing

4 (1)
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
Section 2: Natural Language Representation and Mathematics

This section delves into the preprocessing and text representation aspects of NLP. We will discuss strategies that should be employed as part of natural language data cleaning. After that, we'll deep dive into multiple methodologies that can be used for representing text in the form of numbers capturing syntactical and semantic information.

This section comprises the following chapters:

  • Chapter 3, Building Your NLP Vocabulary
  • Chapter 4, Transforming Text into Data Structures
  • Chapter 5, Word Embeddings and Distance Measurements for Text
  • Chapter 6, Exploring Sentence-, Document-, and Character-Level Embeddings