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Table Of Contents

Natural Language Processing with Python Quick Start Guide

By : Kasliwal

Buy this Book

Natural Language Processing with Python Quick Start Guide

By: Kasliwal

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Getting Started with Text Classification

What is NLP?

NLP workflow template

Example – text classification workflow

Summary

Tidying your Text

Bread and butter – most common tasks

Tokenization

Stemming and lemmatization

spaCy compared with NLTK and CoreNLP

Correcting spelling

Cleaning a corpus with FlashText

Summary

Leveraging Linguistics

Linguistics and NLP

Summary

Text Representations - Words to Numbers

Vectorizing a specific dataset

Word representations

Document embedding

Summary

Modern Methods for Classification

Machine learning for text

Summary

Deep Learning for NLP

What is deep learning?

Understanding deep learning

Putting it all together – the training loop

Kaggle – text categorization challenge

Summary

Building your Own Chatbot

Why chatbots as a learning example?

Quick code means word vectors and heuristics

Summary

Web Deployments

Web deployments

Summary

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Building your Own Chatbot

Chatbots, better referred to as conversation software, are amazing tools for a lot of businesses. They help businesses serve their client's server 24/7 without increasing effort, with consistent quality, and the built-in option to defer to a human when bots are not enough.

They are a great example of where technology and AI has come together to improve the impact of human effort.

They range from voice-based solutions such as Alexa, to text-based Intercom chat boxes, to menu-based navigation in Uber.

A common misconception is that building chatbots needs large teams and a lot of machine learning expertise, though this is true if you are trying to build a generic chatbot platform like Microsoft or Facebook (or even Luis, Wit.ai, and so on).

In this chapter, we will cover the following topics: