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Word Sense Disambiguation (WSD), which aims to identify the correct sense of a given polyseme, is a long-standing problem in NLP.
Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone
Michael Lesk. 1986 · 1986
Earlier work this paper cites.
Using a semantic concordance for sense identification
George A Miller, Martin Chodorow, Shari Landes, Claudia Leacock, and Robert G Thomas. 1994 · 1994
Earlier work this paper cites.
Wordnet: a lexical database for english
George A Miller. 1995 · 1995
Earlier work this paper cites.
Senseval-2: overview
Philip Edmonds and Scott Cotton. 2001 · 2001
Earlier work this paper cites.
The english all-words task
Benjamin Snyder and Martha Palmer. 2004 · 2004
Earlier work this paper cites.
Semeval-2007 task-17: English lexical sample, srl and all words
Sameer Pradhan, Edward Loper, Dmitriy Dligach, and Martha Palmer. 2007 · 2007
Earlier work this paper cites.
Personalizing pagerank for word sense disambiguation
Eneko Agirre and Aitor Soroa. 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.
Word sense disambiguation improves information retrieval
Zhi Zhong and Hwee Tou Ng. 2012 · 2012
Cited alongside, same era.
Semeval-2013 task 12: Multilingual word sense disambiguation
Roberto Navigli, David Jurgens, and Daniele Vannella. 2013 · 2013
Cited alongside, same era.
An enhanced lesk word sense disambiguation algorithm through a distributional semantic model
Pierpaolo Basile, Annalina Caputo, and Giovanni Semeraro. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Entity linking meets word sense disambiguation: a unified approach
Andrea Moro, Alessandro Raganato, and Roberto Navigli. 2014 · 2014
Cited alongside, same era.
Semeval-2015 task 13: Multilingual all-words sense disambiguation and entity linking
Word sense disambiguation using a bidirectional lstm
Mikael Kågebäck and Hans Salomonsson. 2016 · 2016
Later among the works it cites.
context2vec: Learning generic context embedding with bidirectional lstm
Oren Melamud, Jacob Goldberger, and Ido Dagan. 2016 · 2016
Later among the works it cites.
Word sense-aware machine translation: Including senses as contextual features for improved translation models
Steven Neale, Luís Gomes, Eneko Agirre, Oier Lopez de Lacalle, and António Branco. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Later among the works it cites.
Semi-supervised word sense disambiguation with neural models
Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, and Eric Altendorf. 2016 · 2016
Later among the works it cites.
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Andrea Moro and Roberto Navigli. 2015 · 2015
Cited alongside, same era.
One million sense-tagged instances for word sense disambiguation and induction
Kaveh Taghipour and Hwee Tou Ng. 2015 · 2015
Cited alongside, same era.
Embeddings for word sense disambiguation: An evaluation study
Ignacio Iacobacci, Mohammad Taher Pilehvar, and Roberto Navigli. 2016 · 2016
Cited alongside, same era.
Leveraging gloss knowledge in neural word sense disambiguation by hierarchical co-attention
Fuli Luo, Tianyu Liu, Zexue He, Qiaolin Xia, Zhifang Sui, and Baobao Chang. 2018a
Cited in the paper.
Incorporating glosses into neural word sense disambiguation
Fuli Luo, Tianyu Liu, Qiaolin Xia, Baobao Chang, and Zhifang Sui. 2018b
Cited in the paper.
Neural sequence learning models for word sense disambiguation
Alessandro Raganato, Claudio Delli Bovi, and Roberto Navigli. 2017a
Cited in the paper.
Word sense disambiguation: A unified evaluation framework and empirical comparison
Alessandro Raganato, Jose Camacho-Collados, and Roberto Navigli. 2017b
Cited in the paper.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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