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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

Using information from visualization to make decisions about processing

This section includes guidance about how visualization can help us make decisions about processing. For example, in making a decision about whether to remove punctuation and stopwords, exploring word frequency visualizations such as frequency distribution and word clouds can tell us whether very common words are obscuring patterns in the data.

Looking at frequency distributions of words for different categories of data can help rule out simple keyword-based classification techniques.

Frequencies of different kinds of items, such as words and bigrams, can yield different insights. It can also be worth exploring the frequencies of other kinds of items, such as parts of speech or syntactic categories such as noun phrases.

Displaying document similarities with clustering can provide insight into the most meaningful number of classes that you would want to use in dividing a dataset.

The final section summarizes...

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