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

Sentence-level analysis

Sentences can be analyzed in terms of their syntax (the structural relationships among parts of the sentence) or their semantics (the relationships among the meanings of the parts of the sentence). We’ll look at both of these types of analysis next. Recognizing syntactic relationships is useful on its own for applications such as grammar checking (does the subject of the sentence agree with the verb? Is the correct form of the verb being used?), while recognizing semantic relationships on their own is useful for applications such as finding the components of a request in chatbots. Recognizing both syntactic and semantic relationships together is an alternative to statistical methods in almost any NLP application.

Syntactic analysis

The syntax of sentences and phrases can be analyzed in a process called parsing. Parsing is a type of analysis that attempts to match a set of rules, called grammar, to an input text. There are many approaches to parsing...