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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 approaches

The basic idea behind rule-based approaches is that language obeys rules about how words are related to their meanings. For example, when we learn foreign languages, we typically learn specific rules about what words mean, how they’re ordered in sentences, and how prefixes and suffixes change the meanings of words. The rule-based approach to NLU operates on the premise that these kinds of rules can be provided to an NLU system so that the system can determine the meanings of sentences in the same way that a person does.

The rule-based approach was widely used in NLU from the mid-1950s through the mid-1990s until machine-learning-based approaches became popular. However, there are still NLU problems where rule-based approaches are useful, either on their own or when combined with other techniques.

We will begin by reviewing the rules and data that are relevant to various aspects of language.

Words and lexicons

Nearly everyone is familiar with...

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