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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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Machine learning for text

There are at least 10 to 20 machine learning techniques that are well known in the community, ranging from SVMs to several regressions and gradient boosting machines. We will select a small taste of these.

Source: https://www.kaggle.com/surveys/2017.

The preceding graph shows the most popular machine learning techniques used by Kagglers.

We met Logistic Regression in the first chapter while working the 20 newsgroups dataset. We will revisit Logistic Regression and introduce Naive Bayes, SVM, Decision Trees, Random Forests, and XgBoost. XgBoost is a popular algorithm used by several Kaggle winners to achieve award-winning results. We will use the scikit-learn and XGBoost packages in Python to see the previous example in code.