Resource-poor Language Retrieval
Resource-Poor Language Retrieval is a type of cross-language information retrieval approach that specifically addresses the challenge of retrieving information in languages with limited linguistic resources, such as annotated corpora, lexicons, or pre-trained models, by leveraging transfer from resource-rich languages or innovative data-scarce techniques.
28
Publications
2.9K
Citations
109
Authors
42
Institutions
Publications per year
2017–2026
6
Variants
Low-resource Language Retrieval
28
1–5 of 28
109
IBM Research - Thomas J. Watson Research Center, United States
4 publications · 114 citations
4
h-index
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 109
42
Columbia University
New York, United States
3 h-index
9 publications
207 citations
IBM (United States)
Armonk, United States
3 h-index
17 publications
731 citations
Institute for Infocomm Research
Singapore, Singapore
2 h-index
4 publications
53 citations
Brno University of Technology
Brno, Czechia
2 h-index
2 publications
46 citations
IBM Research - Haifa
Haifa, Israel
2 h-index
2 publications
90 citations
RWTH Aachen University
Aachen, Germany
2 h-index
6 publications
180 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
7 publications
316 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
563 citations
1–8 of 42
JAIR · 1 publications · 20 citations
1
h-index
1 publications · 10 citations
1
h-index
1 publications · 16 citations
1
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Repository · 1 publications · 26 citations
1
h-index
1 publications · 30 citations
1
h-index
1–5