Document-level Annotation
Document-Level Annotation is a type of knowledge annotation that characterizes a scholarly document as a whole by identifying and classifying its primary contribution—such as a method, model, framework, theory, construct, dataset, task, or metric—thereby distinguishing it from finer-grained annotation approaches that target individual propositions or passages within the document.
49
Publications
5.9K
Citations
230
Authors
87
Institutions
Publications per year
2017–2026
22
Parents
49
Learning Word Vectors for Sentiment Analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham et al. · 2011 · 3.3K citations
Sumithra Velupillai, Hanna Suominen, Maria Liakata et al. · Journal of Biomedical Informatics · 2018 · 248 citations · Full text
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230
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87
Stanford University
Stanford, United States
5 h-index
17 publications
22.8K citations
University of Utah
Salt Lake City, United States
4 h-index
19 publications
1.6K citations
VA Salt Lake City Healthcare System
Salt Lake City, United States
3 h-index
18 publications
1.3K citations
Sony Computer Science Laboratories
Tokyo, Japan
2 h-index
2 publications
44 citations
Columbia University
New York, United States
2 h-index
4 publications
212 citations
University of Washington
Seattle, United States
2 h-index
2 publications
300 citations

Vanderbilt University
Nashville, United States
2 h-index
9 publications
391 citations
Geriatric Research Education and Clinical Center
Pittsburgh, United States
2 h-index
7 publications
380 citations
1–8 of 87
3 publications · 471 citations
3
h-index
JMI · 2 publications · 48 citations
2
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JAMIA · 2 publications · 150 citations
2
h-index
Repository · 2 publications · 29 citations
2
h-index
Conference · 1 publications · 14 citations
1
h-index
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