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

Distance/similarity calculation between document vectors

We have seen two methods of building vectors to represent text documents. The next question that comes up is:

How can you measure how similar or dissimilar text documents are and how can the vectors built so far be leveraged to have a solution to this problem?

If the words being used in two documents are similar, it indicates that the documents are similar as well. In this section, we will look into cosine similarity and use it to find how similar documents are based on the term vectors.

Cosine similarity

Cosine similarity provides insights into the angle between two vectors. Two vectors would be similar if they are pretty close in terms of both direction and magnitude. We will use techniques developed in the previous sections to build these vectors, and then figure out how close or far they are from each other using cosine similarity.

Cosine similarity helps in measuring the cosine of the angles between two vectors. The value...