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In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
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Ayman Alhelbawy and Robert Gaizauskas. 2014 · 2014
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Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli. 2014 · 2014
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Heng Ji, Nothman Joel, and Hachey Ben. 2014 · 2014
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Surdeanu Mihai Bauer John Finkel Jenny Bethard Steven J. Manning, Christopher D. and David McClosky. 2014 · 2014
Personalized page rank for named entity disambiguation
Maria Pershina, Yifan He, and Ralph Grishman. 2015 · 2015
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Modeling mention, context and entity with neural networks for entity disambiguation
Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, and Xiaolong Wang. 2015 · 2015
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semantic similarity for entity linking with convolutional neural networks
Matthew Francis-Landau, Greg Durrett, and Dan Klein. 2016 · 2016
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Amir Globerson, Nevena Lazic, Soumen Chakrabarti, Amarnag Subramanya, Michael Ringgaard, and Fernando Pereira. 2016 · 2016
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Avirup Sil and Radu Florian. 2016 · 2016
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Michael Ringgaard Nevena Lazic, Amarnag Subramanya and Fernando Pereira. 2015 · 2015
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Joint learning of the embedding of words and entities for named entity disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2016 · 2016
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Deep joint entity disambiguation with local neural attention
Octavian-Eugen Ganea and Thomas Hofmann. 2017 · 2017
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Neural cross-lingual entity linking
Avirup Sil, Gourab Kundu, Radu Florian, and Wael Hamza. 2018 · 2018
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