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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 metrics

There are two important concepts that we should keep in mind when selecting an evaluation metric for NLP systems or, more generally, any system that we want to evaluate:

  • Validity: The first is validity, which means that the metric corresponds to what we think of intuitively as the actual property we want to know about. For example, we wouldn’t want to pick the length of a text as a measurement for its positive or negative sentiment because the length of a text would not be a valid measure of its sentiment.
  • Reliability: The other important concept is reliability, which means that if we measure the same thing repeatedly, we always get the same result.

In the next sections, we will look at some of the most commonly used metrics in NLU that are considered to be both valid and reliable.

Accuracy and error rate

In Chapter 9, we defined accuracy as the number of correct system responses divided by the overall number of inputs. Similarly,...

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