Book Image

Natural Language Understanding with Python

By : Deborah A. Dahl
5 (1)
Book Image

Natural Language Understanding with Python

5 (1)
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)
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 flowchart for deciding on NLU applications

This chapter has covered many considerations that should be taken into account in deciding on an NLP application.

Figure 2.2 summarizes these considerations as a flowchart of the process for evaluating a potential NLU application.

Figure 2.2 – Steps in evaluating an NLU project

Figure 2.2 – Steps in evaluating an NLU project

Starting at the top, the process starts by asking whether the problem is too hard or too easy for the current state of the art, using the criteria discussed earlier. If it’s either too hard or too easy, we should look for another application, or look at cutting back or expanding the scope of the application to make it a better fit for NLP technology. For example, the application might be redesigned to handle fewer languages.

If the problem seems to be a good fit for the state of the art, the next steps are to ensure that the appropriate data is available, and if not, whether data can be collected. Once data is available...