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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

Evaluation paradigms

In this section, we will review some of the major evaluation paradigms that are used to quantify system performance and compare systems.

Comparing system results on standard metrics

This is the most common evaluation paradigm and probably the easiest to carry out. The system is simply given data to process, and its performance is evaluated quantitatively based on standard metrics. The upcoming Evaluation metrics section will delve into this topic in much greater detail.

Evaluating language output

Some NLU applications produce natural language output. These include applications such as translation or summarizing text. They differ from applications with a specific right or wrong answer, such as classification and slot filling, because there is no single correct answer – there could be many good answers.

One way to evaluate machine translation quality is for humans to look at the original text and the translation and judge how accurate it is...

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