Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

Introduction

The previous chapters laid a firm foundation for NLP. But now we will go deeper into a key topic – one that gives us surprising insights into how a language works and how some of the key advances in human computer interaction are facilitated. At the heart of NLP is the simple trick of representing text as numbers. This helps software algorithms to perform the sophisticated computations that are required to understand the meaning of text.

Text representation can be as simple as encoding each word as an integer. But it can also include using an array of numbers for each word. Each of these representations help machine learning programs to function effectively.

This chapter begins by discussing vectors, how text can be represented as vectors, and how vectors can be composed to represent complex speech. We will walk through the various representations in both directions – learning how to encode text as vectors as well as how to retrieve text from vectors...