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Collaborative Annotation For Reliable Natural Language Processing Technica 1st Edition Fort

  • SKU: BELL-5674610
Collaborative Annotation For Reliable Natural Language Processing Technica 1st Edition Fort
$ 31.00 $ 45.00 (-31%)

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Collaborative Annotation For Reliable Natural Language Processing Technica 1st Edition Fort instant download after payment.

Publisher: Wiley-ISTE
File Extension: PDF
File size: 3.71 MB
Pages: 150
Author: Fort, Karòn
ISBN: 9781848219045, 1848219040
Language: English
Year: 2016
Edition: 1

Product desciption

Collaborative Annotation For Reliable Natural Language Processing Technica 1st Edition Fort by Fort, Karòn 9781848219045, 1848219040 instant download after payment.

This book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language Processing (NLP).  NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems.

These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential.

Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject.

Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.

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