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.

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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Text Classification
Getting Started with Text Classification
What is NLP?
NLP workflow template
Example – text classification workflow
Summary
2
Tidying your Text
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
3
Leveraging Linguistics
Leveraging Linguistics
Linguistics and NLP
Summary
4
Text Representations - Words to Numbers
Text Representations - Words to Numbers
Vectorizing a specific dataset
Word representations
Document embedding
Summary
5
Modern Methods for Classification
Modern Methods for Classification
Machine learning for text
Summary
6
Deep Learning for NLP
Deep Learning for NLP
What is deep learning?
Understanding deep learning
Putting it all together – the training loop
Kaggle – text categorization challenge
Summary
7
Building your Own Chatbot
Building your Own Chatbot
Why chatbots as a learning example?
Quick code means word vectors and heuristics
Summary
8
Web Deployments
Web Deployments
Web deployments
Summary
9
Other Books You May Enjoy
Other Books You May Enjoy
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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.

Sentiment analysis as text classification...

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