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We present a simple extension of the GloVe representation learning model that begins with general-purpose representations and updates them based on data from a specialized domain.
Random decision forests
Tin Kam Ho. 1995 · 1995
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SNOMED RT: A reference terminology for health care
Kent A Spackman, Keith E Campbell, and Roger A Côté. 1997 · 1997
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
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Counter-fitting word vectors to linguistic constraints
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2016 · 2016
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Improved semantic representation for domain-specific entities
Mohammad Taher Pilehvar and Nigel Collier. 2016 · 2016
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On the effective use of pretraining for natural language inference
Ignacio Cases, Minh-Thang Luong, and Christopher Potts. 2017 · 2017
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Nils Reimers and Iryna Gurevych. 2017 · 2017
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Retrofitting word vectors to semantic lexicons
Manaal Faruqui, Jesse Dodge, Sujay Kumar Jauhar, Chris Dyer, Eduard Hovy, and Noah A. Smith. 2015 · 2015
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An implementation of GloVe in TensorFlow
Grady Simon. 2017 · 2017
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