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

Generating Text with Markov Chains

An idea is expressed using the words of a language. As ideas are not tangible, it is useful to look at text generation in order to gauge whether a machine can think on its own. The utility of text generation is currently limited to an auto-complete functionality, besides a few negative use cases that we will discuss later in this section. Text can be generated in many different ways, which we will explore using Markov chains. Whether this generated text can correspond to a coherent line of thought is something that we will address later in this section.

Markov Chains

A state space defines all possible states that can exist. A Markov chain consists of a state space and a specific type of successor function. For example, in the case of the simplified state space to describe the weather, the states could be Sunny, Cloudy, or Rainy. The successor function describes how a system in its current state can move to a different state or even continue...