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)

Cleaning a corpus with FlashText

But what about a web-scale corpus with millions of documents and a few thousand keywords? Regex can take several days to run over such exact searches because of its linear time complexity. How can we improve this?

We can use FlashText for this very specific use case:

  • A few million documents with a few thousand keywords
  • Exact keyword matches either by replacing or searching for the presence of those keywords

Of course, there are several different possible solutions to this problem. I recommend this for its simplicity and focus on solving one problem. It does not require us to learn new syntax or set up specific tools such as ElasticSearch.

The following table gives you a comparison of using Flashtext versus compiled regex for searching:

The following tables gives you a comparison of using FlashText versus compiled regex for substitutions...