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Natural Language Understanding with Python

You're reading from  Natural Language Understanding with Python

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Pages 326 pages
Edition 1st Edition
Languages
Author (1):
Deborah A. Dahl Deborah A. Dahl
Profile icon Deborah A. Dahl

Table of Contents (21) Chapters

Preface Part 1: Getting Started with Natural Language Understanding Technology
Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications Chapter 2: Identifying Practical Natural Language Understanding Problems Part 2:Developing and Testing Natural Language Understanding Systems
Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning Chapter 4: Selecting Libraries and Tools for Natural Language Understanding Chapter 5: Natural Language Data – Finding and Preparing Data Chapter 6: Exploring and Visualizing Data Chapter 7: Selecting Approaches and Representing Data Chapter 8: Rule-Based Techniques Chapter 9: Machine Learning Part 1 – Statistical Machine Learning Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models Chapter 12: Applying Unsupervised Learning Approaches Chapter 13: How Well Does It Work? – Evaluation Part 3: Systems in Action – Applying Natural Language Understanding at Scale
Chapter 14: What to Do If the System Isn’t Working Chapter 15: Summary and Looking to the Future Index Other Books You May Enjoy

Representing words with context-independent vectors

So far, we have looked at several ways of representing similarities among documents. However, finding out that two or more documents are similar to each other is not very specific, although it can be useful for some applications, such as intent or document classification. In this section, we will talk about representing the meanings of words with word vectors.

Word2Vec

Word2Vec is a popular library for representing words as vectors, published by Google in 2013 (Mikolov, Tomas; et al. (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/abs/1301.3781). The basic idea behind Word2Vec is that every word in a corpus is represented by a single vector that is computed based on all the contexts (nearby words) in which the word occurs. The intuition behind this approach is that words with similar meanings will occur in similar contexts. This intuition is summarized in a famous quote from the linguist...

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