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)

Text Representations - Words to Numbers

Computers today cannot act on words or text directly. They need to be represented by meaningful number sequences. These long sequences of decimal numbers are called vectors, and this step is often referred to as the vectorization of text.

So, where are these word vectors used:

  • In text classification and summarization tasks
  • During similar word searches, such as synonyms
  • In machine translation (for example, when translating text from English to German)
  • When understanding similar texts (for example, Facebook articles)
  • During question and answer sessions, and general tasks (for example, chatbots used in appointment scheduling)

Very frequently, we see word vectors used in some form of categorization task. For instance, using a machine learning or deep learning model for sentiment analysis, with the following text vectorization methods:

  • TF...