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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 have learned about a number of important strategies to improve the performance of NLU applications. You first learned how to do an initial survey of the data and identify possible problems with the training data. Then, you learned how to find and diagnose problems with accuracy. We then described different strategies to improve performance – specifically, adding data and restructuring the application. The final topic we covered was a review of problems that can occur in deployed applications and how they can be addressed.

In the final chapter, we will provide an overview of the book and a look to the future. We will discuss where there is potential for improvement in the state of the art of NLU performance, as well as faster training, more challenging applications, and what we can expect from NLU technology as the new LLMs become more widely used.

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