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Natural Language Understanding with Python

Natural Language Understanding with Python

By : Deborah A. Dahl
4.8 (13)
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Natural Language Understanding with Python

Natural Language Understanding with Python

4.8 (13)
By: Deborah A. Dahl

Overview of this book

Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Table of Contents (21 chapters)
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1
Part 1: Getting Started with Natural Language Understanding Technology
4
Part 2:Developing and Testing Natural Language Understanding Systems
16
Part 3: Systems in Action – Applying Natural Language Understanding at Scale

Summary

In this chapter, you learned about a number of important topics related to evaluating NLU systems. You learned how to separate data into different subsets for training and testing, and you learned about the most commonly used NLU performance metrics – accuracy, precision, recall, F1, AUC, and confusion matrices – and how to use these metrics to compare systems. You also learned about related topics, such as comparing systems with ablation, evaluation with shared tasks, statistical significance testing, and user testing.

The next chapter will start Part 3 of this book, where we cover systems in action – applying NLU at scale. We will start Part 3 by looking at what to do if a system isn’t working. If the original model isn’t adequate or the system models a real-world situation that changes, what has to be changed? The chapter discusses topics such as adding new data and changing the structure of the application.

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