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Natural Language Understanding with Python

Natural Language Understanding with Python

By : Deborah A. Dahl
4.8 (12)
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Natural Language Understanding with Python

Natural Language Understanding with Python

4.8 (12)
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

User testing

In addition to direct system measurements, it is also possible to evaluate systems with user testing, where test users who are representative of a system’s intended users interact with it.

User testing is a time-consuming and expensive type of testing, but sometimes, it is the only way that you can find out qualitative aspects of system performance – for example, how easy it is for users to complete tasks with a system, or how much they enjoy using it. Clearly, user testing can only be done on aspects of the system that users can perceive, such as conversations, and users should be only expected to evaluate the system as a whole – that is, users can’t be expected to reliably discriminate between the performance of the speech recognition and the NLU components of the system.

Carrying out a valid and reliable evaluation with users is actually a psychological experiment. This is a complex topic, and it’s easy to make mistakes that...

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Natural Language Understanding with Python
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