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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

Machine Learning Part 2 – Neural Networks and Deep Learning Techniques

Neural networks (NNs) have only became popular in natural language understanding (NLU) around 2010 but have since been widely applied to many problems. In addition, there are many applications of NNs to non-natural language processing (NLP) problems such as image classification. The fact that NNs are a general approach that can be applied across different research areas has led to some interesting synergies across these fields.

In this chapter, we will cover the application of machine learning (ML) techniques based on NNs to problems such as NLP classification. We will also cover several different kinds of commonly used NNs—specifically, fully connected multilayer perceptrons (MLPs), convolutional NNs (CNNs), and recurrent NNs (RNNs)—and show how they can be applied to problems such as classification and information extraction. We will also discuss fundamental NN concepts such as hyperparameters...

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