Book Image

Natural Language Processing with Python Quick Start Guide

By : Nirant Kasliwal
Book Image

Natural Language Processing with Python Quick Start Guide

By: Nirant Kasliwal

Overview of this book

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Table of Contents (10 chapters)

Modern Methods for Classification

We now know how to convert text strings to numerical vectors that capture some meaning. In this chapter, we will look at how to use those with embedding. Embedding is the more frequently used term for word vectors and numerical representations.

In this chapter, we are still following the broad outline from our first, that is, text→ representations → models evaluation deployment.

We will continue working with text classification as our example task. This is mainly because it's a simple task for demonstration, but we can also extend almost all of the ideas in this book to solve other problems. The main focus ahead, however, is machine learning for text classification.

To sum up, in this chapter we will be looking at the following topics:

  • Sentiment analysis as a specific class and example of text classification...