Book Image

Natural Language Understanding with Python

By : Deborah A. Dahl
5 (1)
Book Image

Natural Language Understanding with Python

5 (1)
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)
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

Technical requirements

The code that we will go over in this chapter makes use of a number of open source software libraries and resources. We have used many of these in earlier chapters, but we will list them here for convenience:

  • The Tensorflow machine learning libraries: hub, text, and tf-models
  • The Python numerical package, NumPy
  • The Matplotlib plotting and graphical package
  • The IMDb movie reviews dataset
  • scikit-learn’s sklearn.model_selection to do the training, validation, and test split
  • A BERT model from TensorFlow Hub: we’re using this one –'small_bert/bert_en_uncased_L-4_H-512_A-8' – but you can use any other BERT model you like, bearing in mind that larger models might take a long time to train

Note that we have kept the models relatively small here so that they don’t require an especially powerful computer. The examples in this chapter were tested on a Windows 10 machine with an Intel 3.4 GHz...