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Neural Network Methods for Natural Language Processing
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.
The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
312 pages, Paperback
Published April 17, 2017
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Displaying 1 - 7 of 7 reviews
April 23, 2023
This is an amazing intro to NLP that covers all the basics going from count-based approaches to neural nets and explaining in super clear terms the reasoning for each. A must have if you’re doing NLP and better than most of the OReilly/Manning books that cover the topic.
September 17, 2017
A very handy quick introduction to Neural Network methods as they are being used in NLP. If you are familiar with NLP research and want to get a quick refresher on what has been going on, this short book will get you up to speed quickly. Then you can dive into the things of your interest by picking from the extensive bibliography.
August 23, 2017
This book is a MUST have for every student or researcher interested in Natural Language Processing, particularly those transitioning from "classic" statistical techniques in machine learning to both NLP and especially Neural Network methods. An extremely friendly and didactic book, but at the same time rigorous and with broad coverage, with nice bibliography to keep exploring this huge research field!
February 7, 2021
Totally worth my money. Although the book does not describe the latest state of the art considering it has been published before the NLP boom around transformer, and it lacks of the code examples for various techniques, I still love this book's content which is delightful and enlightening to read for various conventional NLP techniques as well as deep learning model based on recurrent and convolutional models
August 1, 2018
Explanations (in English) were v clear/concise and content is up to date.
March 26, 2019
Solid material and presented well.
January 4, 2020
best book for people who wanna learn deep learning in Nlp in details
Displaying 1 - 7 of 7 reviews