You're reading from Natural Language Understanding with Python

Product type Book

Published in Jun 2023

Publisher Packt

ISBN-13 9781804613429

Pages 326 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Deborah A. Dahl

Table of Contents (21) Chapters

Preface

Part 1: Getting Started with Natural Language Understanding Technology

Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications

Chapter 2: Identifying Practical Natural Language Understanding Problems

Part 2:Developing and Testing Natural Language Understanding Systems

Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning

Chapter 4: Selecting Libraries and Tools for Natural Language Understanding

Chapter 5: Natural Language Data – Finding and Preparing Data

Chapter 6: Exploring and Visualizing Data

Chapter 7: Selecting Approaches and Representing Data

Chapter 8: Rule-Based Techniques

Chapter 9: Machine Learning Part 1 – Statistical Machine Learning

Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques

Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models

Chapter 12: Applying Unsupervised Learning Approaches

Chapter 13: How Well Does It Work? – Evaluation

Part 3: Systems in Action – Applying Natural Language Understanding at Scale

Chapter 14: What to Do If the System Isn’t Working

Chapter 15: Summary and Looking to the Future

Index

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Summary

In this chapter, we covered the topic of selecting NLP applications that have a good chance of success with current NLP technology. Successful applications generally have input with specific, objective answers, have training data available, and handle (at most) a few languages.

Specifically, this chapter addressed a number of important questions. We learned how to identify problems that are the appropriate level of difficulty for the current state of the art of NLU technology. We also learned how to ensure that sufficient data is available for system development and how to estimate the costs of development and maintenance.

Learning how to evaluate the feasibility of different types of NLP applications as discussed in this chapter will be extremely valuable as you move forward with your NLP projects. Selecting an application that is too ambitious will result in frustration and a failed project, whereas selecting an application that is too easy for the state of the art...