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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

Rule-based techniques

Rule-based techniques in NLP are, as the name suggests, based on rules written by human developers, as opposed to machine-learned models derived from data. Rule-based techniques were, for many years, the most common approach to NLP, but as we saw in Chapter 7, rule-based approaches have largely been superseded by numerical, machine-learned approaches for the overall design of most NLP applications. There are many reasons for this; for example, since rules are written by humans, it is possible that they might not cover all situations if the human developer has overlooked something.

However, for practical applications, rules can be very useful, either by themselves or, more likely, along with machine-learned models.

The next section will discuss the motivations for using rules in NLP applications.

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