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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

Taking development costs into account

After making sure that data is available, and that the data is (or can be) annotated with the required intents, entities, and classification categories, the next consideration for deciding whether NLP is a good fit for an application is the cost of developing the application itself. Some technically feasible applications can nevertheless be impractical because they would be too costly, risky, or time-consuming to develop.

Development costs include determining the most effective machine learning approaches to a specific problem. This can take significant time and involve some trial and error as models need to be trained and retrained in the process of exploring different algorithms. Identifying the most promising algorithms is also likely to require NLP data scientists, who may be in short supply. Developers have to ask the question of whether the cost of development is consistent with the benefits that will be realized by the final application...

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