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

Understanding vectors and matrices

The introduction to this chapter touched upon the challenge of representing text data in a mathematical form. Two of the most popular data structures used with text data are vectors and matrices. We will now have a look at each one of these in detail.

Vectors

Vectors are a one-dimensional array of numbers in which each number could be identified by its respective indices. They are typically represented as a column enclosed in square brackets, as shown here:

In this example, the x vector has three elements, and these three elements store information about the vector. Mathematicians abstract vectors as an object in space, where each element of the vector represents the projection of that vector along a given axis. We often use the term Rn to define a vector, where R is a representation mechanism and n denotes the number of dimensions used to describe the vector. In general, Rn is the set of all n-tuples of real numbers.

In the preceding example, the...