Transformation-based Learning
Transformation-Based Learning is a supervised machine learning methodology within computational linguistics that iteratively applies rule-based transformations to annotate parts of speech, distinguished by its use of error-driven learning to refine tagging accuracy through successive rule application.
135
Publications
8.4K
Citations
331
Authors
141
Institutions
Publications per year
2017–2026
29
Parents
135
Text Chunking using Transformation-Based Learning
Lance Ramshaw, Mitchell P. Marcus · ArXiv.org · 1995 · 474 citations · Full text
Named entity recognition through classifier combination
Radu Florian, Abe Ittycheriah, Hongyan Jing et al. · 2003 · 403 citations · Full text
Deep learning-based transformation of H&E stained tissues into special stains
Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman et al. · Nature Communications · 2021 · 297 citations · Full text
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Johns Hopkins University
Baltimore, United States
9 h-index
18 publications
2.8K citations
University of Delaware
Newark, United States
5 h-index
13 publications
550 citations
University of Pennsylvania
Philadelphia, United States
3 h-index
6 publications
432 citations
Mitre (United States)
Bedford, United States
3 h-index
3 publications
128 citations
University of Maryland, College Park
College Park, United States
3 h-index
6 publications
173 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
7 publications
405 citations

University of Manchester
Manchester, United Kingdom
3 h-index
6 publications
141 citations
Stanford University
Stanford, United States
2 h-index
8 publications
1K citations
1–8 of 141
Repository · 10 publications · 303 citations
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Repository · 7 publications · 833 citations
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Conference · 4 publications · 69 citations
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3 publications · 207 citations
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2 publications · 31 citations
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