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)


In this chapter, we looked at several new ideas regarding machine learning. The intention here was to demonstrate some of the most common classifiers. We looked at how to use them with one thematic idea: translating text to a numerical representation and then feeding that to a classifier.

This chapter covered a fraction of the available possibilities. Remember, you can try anything from better feature extraction using Tfidf to tuning classifiers with GridSearch and RandomizedSearch, as well as ensembling several classifiers.

This chapter was mostly focused on pre-deep learning methods for both feature extraction and classification.

Note that deep learning methods also allow us to use a single model where the feature extraction and classification are both learned from the underlying data distribution. While a lot has been written about deep learning in computer vision...