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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

Identifying Practical Natural Language Understanding Problems

In this chapter, you will learn how to identify natural language understanding (NLU) problems that are a good fit for today’s technology. That means they will not be too difficult for the state-of-the-art NLU approaches but neither can they be addressed by simple, non-NLU approaches. Practical NLU problems also require sufficient training data. Without sufficient training data, the resulting NLU system will perform poorly. The benefits of an NLU system also must justify its development and maintenance costs. While many of these considerations are things that project managers should think about, they also apply to students who are looking for class projects or thesis topics.

Before starting a project that involves NLU, the first question to ask is whether the goals of the project are a good fit for the current state of the art in NLU. Is NLU the right technology for solving the problem that you wish to address?...

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