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Neural Network Methods for Natural Language Processing
Table of Contents:
Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography
Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography
309 pages, ebook
Published April 17, 2017
29 people are currently reading
241 people want to read
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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