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

Recent Developments in Text Generation and Summarization

Alan Turing (for whom the equivalent of the Nobel Prize in Computer Science is named) proposed a test for artificial intelligence in 1950. This test, known as the Turing Test, says that if humans ask questions and cannot distinguish between text responses generated by a machine and a human, then that machine can be deemed to be intelligent.

Text generation using very large models, such as the GPT-2 (with around 1.5 billion parameters) and BERT (Bidirectional Encoder Representation from Transformers) (with around 340 million parameters), can aid in auto-completion tasks. Auto-completion presents unique ethical challenges. While it can offer convenience, it can also reinforce biases in the data. This is accentuated by the fact that most user experience layouts can show only a limited number of options. Furthermore, auto-completion can controversially suggest responses that are different from what the sender originally wants...