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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

Selecting Approaches and Representing Data

This chapter will cover the next steps in getting ready to implement a natural language processing (NLP) application. We start with some basic considerations about understanding how much data is needed for an application, what to do about specialized vocabulary and syntax, and take into account the need for different types of computational resources. We then discuss the first steps in NLP – text representation formats that will get our data ready for processing with NLP algorithms. These formats include symbolic and numerical approaches for representing words and documents. To some extent, data formats and algorithms can be mixed and matched in an application, so it is helpful to consider data representation independently from the consideration of algorithms.

The first section will review general considerations for selecting NLP approaches that have to do with the type of application we’re working on, and with the data that...

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