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Natural Language Annotation for Machine Learning 1st (first) Edition by James Pustejovsky, Amber Stubbs published by O'Reilly Media
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you M odel, A nnotate, T rain, T est, E valuate, and R evise your training corpus. You also get a complete walkthrough of a real-world annotation project. This book is a perfect companion to O’Reilly’s Natural Language Processing with Python .
Unknown Binding
First published January 1, 2012
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Displaying 1 - 9 of 9 reviews
to-keep-reference
November 8, 2018...provides a slightly more advanced theoretical guide. You’ll need knowledge of Python to implement the lessons; the topics covered work perfectly with Python’s Natural Language Toolkit.
á.149
á.149
May 1, 2020
good first part about annotations, a little bit outdated about ML
November 3, 2018
I wish I had read this book for my master thesis which was exactly what this book teaches: Create a NLP annotation project on a corpus. It is really good for those just starting out with NLP. I think the title of the book should get a keyword such as "introduction" or "for beginners" because it only covers the basics of NLP annotation.
However, there are a few issues that hinder a five star rating:
First, it is out of date. Quite a few links in the book are deprecated and lead nowhere. Some of the projects introduced no longer exist. As of the time of writing this review in 2018 the book is six years old and research has moved on. It really needs a revision.
Second, it reads as dry as a research paper. Because it is written by researchers. Be prepared to skip vast sections to keep your sanity. Be aware that the summary in some of the chapters omit vital parts of the chapter while inexplicably include minor details.
On the other hand I have not found any other ressource as comprehensive as this. As long as there is nothing better, this seems to be your introduction to NLP.
However, there are a few issues that hinder a five star rating:
First, it is out of date. Quite a few links in the book are deprecated and lead nowhere. Some of the projects introduced no longer exist. As of the time of writing this review in 2018 the book is six years old and research has moved on. It really needs a revision.
Second, it reads as dry as a research paper. Because it is written by researchers. Be prepared to skip vast sections to keep your sanity. Be aware that the summary in some of the chapters omit vital parts of the chapter while inexplicably include minor details.
On the other hand I have not found any other ressource as comprehensive as this. As long as there is nothing better, this seems to be your introduction to NLP.
November 27, 2020
A good starting point to understand the NLP dataset annotation process
February 22, 2017
Если у вас нет ни малейшего представления, что такое корпус и какие в принципе существуют походы к его разметке, то эта книга станет отличной отправной точкой для наращивания своих знаний в этой области.
Из минусов: на мой вкус много воды - можно было бы сделать подачу более концентрированной; опять же содержит только самые базовые вещи. Если вы неравнодушны к теме машобуча и уже хоть что-то читали об этом в виде статей или обзоров, будет скучновато.
Из плюсов: есть много ссылок на общедоступные ресурсы, на готовые корпуса, на широко применяемые практики, есть примеры того как делать правильно и как делать плохо, но можно. Приятно оформлена, есть иллюстрации программ, схемы и таблицы читабельны.
Из минусов: на мой вкус много воды - можно было бы сделать подачу более концентрированной; опять же содержит только самые базовые вещи. Если вы неравнодушны к теме машобуча и уже хоть что-то читали об этом в виде статей или обзоров, будет скучновато.
Из плюсов: есть много ссылок на общедоступные ресурсы, на готовые корпуса, на широко применяемые практики, есть примеры того как делать правильно и как делать плохо, но можно. Приятно оформлена, есть иллюстрации программ, схемы и таблицы читабельны.
October 8, 2014
This book is fairly basic if you have already generated your own corpus for training models.
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December 19, 2014Location: GG6 IRC
Accession no: DL026658
Accession no: DL026658
July 27, 2016
The book is good for beginners in NLP. it's more of NLP than ML. Learned about Annotating processes and how it's done.
Good book in general :).
Good book in general :).
Read
December 17, 2014GG6 DL026658
Displaying 1 - 9 of 9 reviews
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