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

Basics of NNs

The basic concepts behind NNs have been studied for many years but have only fairly recently been applied to NLP problems on a large scale. Currently, NNs are one of the most popular tools for solving NLP tasks. NNs are a large field and are very actively researched, so we won’t be able to give you a comprehensive understanding of NNs for NLP. However, we will attempt to provide you with some basic knowledge that will let you apply NNs to your own problems.

NNs are inspired by some properties of the animal nervous system. Specifically, animal nervous systems consist of a network of interconnected cells, called neurons, that transmit information throughout the network with the result that, given an input, the network produces an output that represents a decision about the input.

Artificial NNs (ANNs) are designed to model this process in some respects. The decision about how to react to the inputs is determined by a sequence of processing steps starting with...