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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

Looking at an example

To illustrate some of these concepts, we’ll work through an example using JupyterLab where we explore an SA task for movie reviews. We’ll look at how we can apply the NLTK and spaCy packages to get some ideas about what the data is like, which will help us plan further processing.

The corpus (or dataset) that we’ll be looking at is a popular set of 2,000 movie reviews, classified as to whether the writer expressed a positive or negative sentiment about the movie (http://www.cs.cornell.edu/people/pabo/movie-review-data/).

Dataset citation

Bo Pang and Lillian Lee, Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, Proceedings of the ACL, 2005.

This is a good example of the task of SA, which was introduced in Chapter 1.

Setting up JupyterLab

We’ll be working with JupyterLab, so let’s start it up. As we saw earlier, you can start JupyterLab by simply typing the...

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