Neural Coreference Resolution
Neural Coreference Resolution is a machine learning approach to coreference resolution that employs neural network architectures to model and predict co-reference relations between textual expressions by learning distributed representations of linguistic context.
54
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2K
Citations
174
Authors
72
Institutions
Publications per year
2017–2026
46
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Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Shany Barhom, Vered Shwartz, Alon Eirew et al. · 2019 · 107 citations · Full text
Dynamic Entity Representations in Neural Language Models
Yangfeng Ji, Chenhao Tan, Sebastian Martschat et al. · 2017 · 98 citations · Full text
End-to-end Neural Coreference Resolution
Kenton Lee, Luheng He, Mike Lewis et al. · 2017 · 84 citations · Full text
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University of Washington
Seattle, United States
4 h-index
10 publications
631 citations

Johns Hopkins University
Baltimore, United States
4 h-index
9 publications
246 citations
Google (United States)
Mountain View, United States
2 h-index
5 publications
1K citations

Bar-Ilan University
Ramat Gan, Israel
2 h-index
4 publications
347 citations
Seattle University
Seattle, United States
2 h-index
5 publications
213 citations
National Research Council
Rome, Italy
2 h-index
11 publications
194 citations

Iwate Prefectural University
Takizawa, Japan
2 h-index
2 publications
36 citations
HUTECH University
Ho Chi Minh City, Vietnam
2 h-index
2 publications
36 citations
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Repository · 6 publications · 185 citations
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2 publications · 36 citations
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2 publications · 56 citations
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1 publications · 55 citations
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Repository · 1 publications · 26 citations
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