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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

A quick overview of evaluation

Before we look at how different statistical techniques work, we have to have a way to measure their performance, and there are a couple of important considerations that we should review first. The first consideration is the metric or score that we assign to the system’s processing. The most common and simple metric is accuracy, which is the number of correct responses divided by the overall number of attempts. For example, if we’re attempting to measure the performance of a movie review classifier, and we attempt to classify 100 reviews as positive or negative, if the system classifies 75 reviews correctly, the accuracy is 75%. A closely related metric is error rate, which is, in a sense, the opposite of accuracy because it measures how often the system made a mistake. In this example, the error rate would be 25%.

We will only make use of accuracy in this chapter, although there are more precise and informative metrics that are actually...

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