Salience Modeling
Salience Modeling is a computational framework within coreference resolution that identifies and ranks discourse entities based on their prominence in context, distinguishing itself by integrating linguistic, positional, and attentional cues to determine which entities are most likely to be referred to in subsequent expressions.
104
Publications
5.1K
Citations
260
Authors
113
Institutions
Publications per year
2017–2026
30
Parents
104
Switchable Deep Network for Pedestrian Detection
Ping Luo, Yonglong Tian, Xiaogang Wang et al. · 2014 · 236 citations
Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer · Annual Review of Economics · 2022 · 226 citations
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113
University of Southern California
Los Angeles, United States
5 h-index
13 publications
215 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
8 publications
459 citations
Michigan State University
East Lansing, United States
4 h-index
5 publications
156 citations
California Southern University
Chandler, United States
4 h-index
11 publications
173 citations
Google (United States)
Mountain View, United States
4 h-index
9 publications
579 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
9 publications
246 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
3 h-index
8 publications
1.1K citations
Yale University
New Haven, United States
3 h-index
3 publications
125 citations
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Repository · 9 publications · 258 citations
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PRQ · 4 publications · 184 citations
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Repository · 3 publications · 69 citations
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3 publications · 123 citations
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Repository · 2 publications · 21 citations
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