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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

Exploring regular expressions

Regular expressions are a widely used rule-based technique that is often used for recognizing fixed expressions. By fixed expressions, we mean words and phrases that are formed according to their own internal rules, which are largely different from the normal patterns of the language.

One type of fixed expression is monetary amounts. There are only a few variations in formats for monetary amounts – the number of decimal places, the symbol for the type of currency, and whether the numbers are separated by commas or periods. The application might only have a requirement to recognize specific currencies, which would simplify the rules further. Other common fixed expressions include dates, times, telephone numbers, addresses, email addresses, measurements, and numbers. Regular expressions in NLP are most frequently used in preprocessing text that will be further analyzed with other techniques.

Different programming languages have slightly different...

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