Semi-Supervised Morphological Segmentation is a morphological segmentation approach that belongs to the class of semi-supervised learning methods and is distinguished by its utilization of both limited labeled data and abundant unlabeled data to infer morphological structure in word forms.
15
Publications
629
Citations
50
Authors
28
Institutions
Publications per year
2017–2026
5
Parents
Publications
15
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
1–5 of 15
Authors
50
- SV
6
h-index
- MK
4
h-index
- OK
4
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- PS
2
h-index
- TR
2
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1–5 of 50
Institutions
28
Aalto University
Espoo, Finland
4 h-index
13 publications
763 citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
4 publications
135 citations
University of Technology
Korolyov, Russia
2 h-index
5 publications
356 citations
Chinese Academy of Sciences
Beijing, China
2 h-index
9 publications
235 citations
Shenzhen Institutes of Advanced Technology
Shenzhen, China
2 h-index
9 publications
235 citations
Guangdong Medical College
Zhanjiang, China
2 h-index
2 publications
55 citations
Signal Processing (United States)
Rockville, United States
1 h-index
3 publications
315 citations
Medical University of Vienna
Vienna, Austria
1 h-index
6 publications
336 citations
1–8 of 28
Venues
- Computers in Biology and Medicine
1 publications · 15 citations
1
h-index
- ACM Transactions on Asian and Low-Resource Language Information Processing
1 publications · 11 citations
1
h-index
- Displays
1 publications · 40 citations
1
h-index
- Computerized Medical Imaging and Graphics
CMIG · 1 publications · 41 citations
1
h-index
- International Conference on Computational Linguistics
Conference · 1 publications · 76 citations
1
h-index
1–5