Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

Supervised Learning

Unlike unsupervised learning, supervised learning algorithms need labeled data. They learn how to automatically generate labels or predict values by analyzing various features of the data provided. For example, say you have already starred important text messages on your phone, and you want to automate the task of going through all your messages daily (considering they are important and marked already). This is a use case for supervised learning. Here, messages that have been starred previously can be used as labeled data. Using this data, you can create two types of models that are capable of the following:

  • Classifying whether new messages are important
  • Predicting the probability of new messages being important

The first type is called classification, while the second type is called regression. Let's learn about classification first.

Classification

Say you have two types of food, of which type 1 tastes sweet and type 2 tastes salty...