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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

Making the most of data with weak supervision

In between completely supervised and unsupervised learning are several approaches to partial supervision, where only x data is supervised. Like unsupervised approaches, the goal of these techniques is to make the most of supervised data, which can be expensive to obtain. One advantage of partial supervision over unsupervised approaches is that unsupervised results don’t automatically have useful labels. The labels have to be supplied, either manually or through some of the techniques we saw earlier in this chapter. In general, with weak supervision, the labels are supplied based on the subset of the data that is supervised.

This is an active research area, and we will not go into it in detail. However, it is useful to know what the general tactics of weak supervision are so that you will be able to apply them as they relate to specific tasks, depending on the kind of labeled data that is available.

Some tactics for weak supervision...

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