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Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
Earlier work this paper cites.
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Hwee Tou Ng and Hian Beng Lee. 1996 · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Statistical phrase-based translation
Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
Earlier work this paper cites.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Senselearner: Minimally supervised word sense disambiguation for all words in open text
Rada Mihalcea and Ehsanul Faruque. 2004 · 2004
Earlier work this paper cites.
Word-sense disambiguation for machine translation
David Vickrey, Luke Biewald, Marc Teyssier, and Daphne Koller. 2005 · 2005
Earlier work this paper cites.
Word sense disambiguation improves statistical machine translation
Yee Seng Chan, Hwee Tou Ng, and David Chiang. 2007 · 2007
Earlier work this paper cites.
Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007 · 2007
Earlier work this paper cites.
Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
Earlier work this paper cites.
Word sense disambiguation: A survey
Roberto Navigli. 2009 · 2009
Earlier work this paper cites.
It makes sense: A wide-coverage word sense disambiguation system for free text
Zhi Zhong and Hwee Tou Ng. 2010 · 2010
Earlier work this paper cites.
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Antonio Di Marco and Roberto Navigli. 2013 · 2013
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Chris Dyer, Victor Chahuneau, and Noah A Smith. 2013 · 2013
Cited alongside, same era.
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Xinxiong Chen, Zhiyuan Liu, and Maosong Sun. 2014 · 2014
Cited alongside, same era.
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Graham Neubig, Makoto Morishita, and Satoshi Nakamura. 2015 · 2015
Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Semi-supervised word sense disambiguation with neural models
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Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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How phrase sense disambiguation outperforms word sense disambiguation for statistical machine translation
Marine Carpuat and Dekai Wu. 2007a
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Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, and Eric Altendorf. 2016 · 2016
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Massive exploration of neural machine translation architectures
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