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1 |
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|a Fort, Karen.
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| 245 |
1 |
0 |
|a Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects /
|c Karen Fort.
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| 264 |
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1 |
|a London :
|b John Wiley & Sons,
|c 2016.
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| 300 |
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|a 1 online resource (vii, 164 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Cognitive Science Series,
|x 2051-249X
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| 588 |
0 |
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|a Print version record.
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| 504 |
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|a Includes bibliographical references and index.
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| 505 |
8 |
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|a A.1.7. Glozz -- A.1.8. CCASH -- A.1.9. brat -- A.2. Task-oriented tools -- A.2.1. LDC tools -- A.2.2. EasyRef -- A.2.3. Phrase Detectives -- A.2.4. ZombiLingo -- A.3. NLP annotation platforms -- A.3.1. GATE -- A.3.2. EULIA -- A.3.3. UIMA -- A.3.4. SYNC3 -- A.4. Annotation management tools -- A.4.1. Slate -- A.4.2. Djangology -- A.4.3. GATE Teamware -- A.4.4. WebAnno -- A.5. (Many) Other tools -- Glossary -- Bibliography -- Index -- Other titles from ISTE in Cognitive Science and Knowledge Management -- ELUA.
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| 520 |
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|a 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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| 650 |
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|a Natural language processing (Computer science)
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| 650 |
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7 |
|a COMPUTERS
|x General.
|2 bisacsh
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| 650 |
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|a Natural language processing (Computer science)
|2 fast
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| 776 |
0 |
8 |
|i Print version:
|a Fort, Karen.
|t Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects.
|d London John Wiley & Sons 2016
|z 1848219040
|w (OCoLC)951504119
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| 830 |
|
0 |
|a Cognitive science series.
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=4558125
|y Click for online access
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| 880 |
8 |
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|6 505-00/(S
|a 1.3.3. Addressing the new annotation challenges -- 1.3.3.1. Towards more flexible and more generic tools -- 1.3.3.2. Towards more collaborative annotation -- 1.3.3.3. Towards the annotation campaign management -- 1.3.4. The impossible dream tool -- 1.4. Evaluating the annotation quality -- 1.4.1. What is annotation quality-- 1.4.2. Understanding the basics -- 1.4.2.1. How lucky can you get-- 1.4.2.2. The kappa family -- 1.4.2.2.1. Scott's pi -- 1.4.2.2.2. Cohen's kappa -- 1.4.2.3. The dark side of kappas -- 1.4.2.4. The F-measure: proceed with caution -- 1.4.3. Beyond kappas -- 1.4.3.1. Weighted coefficients -- 1.4.3.2. γ: the (nearly) universal metrics -- 1.4.4. Giving meaning to the metrics -- 1.4.4.1. The Corpus Shuffling Tool -- 1.4.4.2. Experimental results -- 1.4.4.2.1. Artificial annotations -- 1.4.4.2.2. Annotations from a real corpus -- 1.5. Conclusion -- 2: Crowdsourcing Annotation -- 2.1. What is crowdsourcing and why should we be interested in it-- 2.1.1. A moving target -- 2.1.2. A massive success -- 2.2. Deconstructing the myths -- 2.2.1. Crowdsourcing is a recent phenomenon -- 2.2.2. Crowdsourcing involves a crowd (of non-experts) -- 2.2.3. "Crowdsourcing involves (a crowd of) non-experts" -- 2.3. Playing with a purpose -- 2.3.1. Using the players' innate capabilities and world knowledge -- 2.3.2. Using the players' school knowledge -- 2.3.3. Using the players' learning capacities -- 2.4. Acknowledging crowdsourcing specifics -- 2.4.1. Motivating the participants -- 2.4.2. Producing quality data -- 2.5. Ethical issues -- 2.5.1. Game ethics -- 2.5.2. What's wrong with Amazon Mechanical Turk-- 2.5.3. A charter to rule them all -- Conclusion -- Appendix: (Some) Annotation Tools -- A.1. Generic tools -- A.1.1. Cadixe -- A.1.2. Callisto -- A.1.3. Amazon Mechanical Turk -- A.1.4. Knowtator -- A.1.5. MMAX2 -- A.1.6. UAM CorpusTool.
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|a EBC-AC
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|a 92
|b HCD
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