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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

Summary

In this chapter, we’ve learned how to select different NLP approaches, based on the available data and other requirements. In addition, we’ve learned about representing data for NLP applications. We’ve placed particular emphasis on vector representations, including vector representations of both documents and words. For documents, we’ve covered binary bag of words, count bag of words, and TF-IDF. For representing words, we’ve reviewed the Word2Vec approach and briefly introduced context-dependent vectors, which will be covered in much more detail in Chapter 11.

In the next four chapters, we will take the representations that we’ve learned about in this chapter and show how to train models from them that can be applied to different problems such as document classification and intent recognition. We will start with rule-based techniques in Chapter 8, discuss traditional machine learning techniques in Chapter 9, talk about neural networks...

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