Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer

by Nirant Kasliwal

Overview

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.

Product Details

ISBN-13:	9781788994101
Publisher:	Packt Publishing
Publication date:	11/30/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	182
File size:	2 MB

About the Author

Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.

Table of Contents

Table of Contents

1. Getting Started with Text Classification
2. Tidying your Text
3. Leveraging Linguistics
4. Text Representations - Words to Numbers
5. Modern Methods for Classification
6. Deep Learning for NLP
7. Building your own Chatbot
8. Web Deployments