Neural Representations of Natural Language
Authors: White, L., Togneri, R., Liu, W., Bennamoun, M.
Free Preview- Enriches readers’ understanding of how neural networks create a machine interpretable representation of the meaning of natural language
- Absolutely packed with useful insights drawn from experience using and implementing these algorithms
- Includes two introductory chapters on neural networks, allowing novice readers to quickly understand how machine learning is revolutionizing the field of natural language processing
Buy this book
- About this book
-
This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.
- Table of contents (6 chapters)
-
-
Introduction to Neural Networks for Machine Learning
Pages 1-21
-
Recurrent Neural Networks for Sequential Processing
Pages 23-36
-
Word Representations
Pages 37-71
-
Word Sense Representations
Pages 73-92
-
Sentence Representations and Beyond
Pages 93-114
-
Table of contents (6 chapters)
Recommended for you
Bibliographic Information
- Bibliographic Information
-
- Book Title
- Neural Representations of Natural Language
- Authors
-
- Lyndon White
- Roberto Togneri
- Wei Liu
- Mohammed Bennamoun
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 783
- Copyright
- 2019
- Publisher
- Springer Singapore
- Copyright Holder
- Springer Nature Singapore Pte Ltd.
- eBook ISBN
- 978-981-13-0062-2
- DOI
- 10.1007/978-981-13-0062-2
- Hardcover ISBN
- 978-981-13-0061-5
- Softcover ISBN
- 978-981-13-4320-9
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- XIV, 122
- Number of Illustrations
- 5 b/w illustrations, 31 illustrations in colour
- Topics
*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.
