Semi-supervised Morphological Analysis
Semi-Supervised Morphological Analysis is a morphological parsing methodology that belongs to the class of semi-supervised learning approaches in natural language processing, distinguished by its utilization of a small amount of manually annotated morphological data alongside a larger body of unannotated text to induce morphological structure and relationships in a computationally efficient and linguistically informed manner.
14
Publications
489
Citations
32
Authors
12
Institutions
Publications per year
2017–2026
3
Parents
14
Morfessor 2.0: Toolkit for statistical morphological segmentation
Peter Smit, Sámi Virpioja, Stig-Arne Grönroos et al. · 2014 · 105 citations · Full text
Semi-Supervised Learning of Concatenative Morphology
Oskar Kohonen, Sámi Virpioja, Krista Lagus · 2010 · 61 citations
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Aalto University
Espoo, Finland
4 h-index
13 publications
763 citations
University of Technology
Korolyov, Russia
2 h-index
5 publications
356 citations

University of Szeged
Szeged, Hungary
1 h-index
2 publications
32 citations

Johns Hopkins University
Baltimore, United States
1 h-index
4 publications
44 citations
Universidad Nacional Autónoma de México
Mexico City, Mexico
1 h-index
3 publications
60 citations

Prague University of Economics and Business
Prague, Czechia
1 h-index
3 publications
39 citations
IBM (United States)
Armonk, United States
1 h-index
1 publications
18 citations
University of Stuttgart
Stuttgart, Germany
1 h-index
4 publications
64 citations
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Repository · 1 publications · 16 citations
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Conference · 1 publications · 76 citations
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1 publications · 13 citations
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1 publications · 20 citations
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Conference · 1 publications · 18 citations
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