Rule-based Generation
Rule-Based Generation is a natural language generation approach that systematically produces text by applying predefined linguistic and syntactic rules, distinguished from statistical or neural methods through its reliance on explicitly encoded grammatical and semantic constraints.
324
Publications
14.6K
Citations
869
Authors
389
Institutions
Publications per year
2017–2026
81
324
1–5 of 324
869
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389
Carnegie Mellon University
Pittsburgh, United States
8 h-index
13 publications
762 citations

Indian Statistical Institute
Kolkata, India
7 h-index
16 publications
924 citations
Columbia University
New York, United States
6 h-index
10 publications
457 citations

Peking University
Beijing, China
5 h-index
11 publications
157 citations
Kyushu Institute of Technology
Kitakyushu, Japan
5 h-index
9 publications
396 citations

University of Edinburgh
Edinburgh, United Kingdom
5 h-index
8 publications
282 citations

University of Minnesota
Minneapolis, United States
4 h-index
14 publications
854 citations

Josai International University
Tōgane, Japan
4 h-index
4 publications
118 citations
1–8 of 389
TKDE · 5 publications · 879 citations
5
h-index
4 publications · 138 citations
4
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Repository · 4 publications · 83 citations
4
h-index
3 publications · 187 citations
3
h-index
TPAMI · 3 publications · 262 citations
3
h-index
1–5