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

Problems after deployment

After an NLU system is developed and put into place in an application, it still requires monitoring. Once the system has reached an acceptable level of performance and has been deployed, it can be tempting to leave it alone and assume that it doesn’t need any more attention, but this is not the case. At the very least, the deployed system will receive a continuous stream of new data that can be challenging to the existing system if it is different from the training data in some way. On the other hand, if it is not different, it can be used as new training data. Clearly, it is better to detect performance problems from internal testing than to learn about them from negative customer feedback.

At a high level, we can think of new performance problems as either being due to a change in the system itself, or due to a change in the deployment context.

Changes in system performance due to system changes should be detected by testing before the new system...