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Natural Language Understanding with Python

You're reading from  Natural Language Understanding with Python

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Pages 326 pages
Edition 1st Edition
Languages
Author (1):
Deborah A. Dahl Deborah A. Dahl
Profile icon Deborah A. Dahl

Table of Contents (21) Chapters

Preface Part 1: Getting Started with Natural Language Understanding Technology
Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications Chapter 2: Identifying Practical Natural Language Understanding Problems Part 2:Developing and Testing Natural Language Understanding Systems
Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning Chapter 4: Selecting Libraries and Tools for Natural Language Understanding Chapter 5: Natural Language Data – Finding and Preparing Data Chapter 6: Exploring and Visualizing Data Chapter 7: Selecting Approaches and Representing Data Chapter 8: Rule-Based Techniques Chapter 9: Machine Learning Part 1 – Statistical Machine Learning Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models Chapter 12: Applying Unsupervised Learning Approaches Chapter 13: How Well Does It Work? – Evaluation Part 3: Systems in Action – Applying Natural Language Understanding at Scale
Chapter 14: What to Do If the System Isn’t Working Chapter 15: Summary and Looking to the Future Index Other Books You May Enjoy

Slot-filling with CRFs

In Chapter 8, we discussed the popular application of slot-filling, and we used the spaCy rule engine to find slots for the restaurant search application shown in Figure 8.9. This required writing rules for finding the fillers of each slot in the application. This approach can work fairly well if the potential slot fillers are known in advance, but if they aren’t known in advance, it won’t be possible to write rules. For example, with the rules in the code following Figure 8.9, if a user asked for a new cuisine, say, Thai, the rules wouldn’t be able to recognize Thai as a new filler for the CUISINE slot, and wouldn’t be able to recognize not too far away as a filler for the LOCATION slot. Statistical methods, which we will discuss in this section, can help with this problem.

With statistical methods, the system does not use rules but looks for patterns in its training data that can be applied to new examples. Statistical methods...

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