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)

Web deployments

This is the hackathon version, and more experienced engineers will notice that we neglect a lot of best practices in favor of saving developer time. In my defense, I did add pretty usable logging.

We will start from where we left off when we talked about text classification using machine learning methods. There are a few challenges that we left untouched:

  • Model persistence: How can I write the model, data, and code to disk?
  • Model loading and prediction: How can I load the model data and code from disk?
  • Flask for REST endpoints: How can I expose the loaded model over the web?

If there is anything that you take away from this chapter, it should be the preceding three questions. If you have a clear and complete idea regarding how to tackle these three questions, your battle is won.

We will use a scikit-learn model and the same TF-IDF based pipelines we are familiar...