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The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding.
KERMIT: Generative insertion-based modeling for sequences
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RoBERTa: A robustly optimized bert pretraining approach
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A semantic concordance
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I don’t believe in word senses
Adam Kilgarriff. 1997 · 1997
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WordNet: An electronic lexical database
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SENSEVAL-2: Overview
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Supersense tagging of unknown nouns in WordNet
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The English all-words task
Benjamin Snyder and Martha Palmer. 2004 · 2004
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SemEval-2007 task-17: English lexical sample, SRL and all words
Sameer Pradhan, Edward Loper, Dmitriy Dligach, and Martha Palmer. 2007 · 2007
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Word sense disambiguation: A survey
Roberto Navigli. 2009 · 2009
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Super-sense tagging using support vector machines and distributional features
Pierpaolo Basile. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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SemEval-2013 task 12: Multilingual word sense disambiguation
Roberto Navigli, David Jurgens, and Daniele Vannella. 2013 · 2013
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A unified model for word sense representation and disambiguation
Xinxiong Chen, Zhiyuan Liu, and Maosong Sun. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Lexical semantic analysis in natural language text
Nathan Schneider. 2014 · 2014
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SemEval-2015 task 13: Multilingual all-words sense disambiguation and entity linking
Andrea Moro and Roberto Navigli. 2015 · 2015
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AutoExtend: Extending word embeddings to embeddings for synsets and lexemes
Sascha Rothe and Hinrich Schütze. 2015 · 2015
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A corpus and model integrating multiword expressions and supersenses
Nathan Schneider and Noah A. Smith. 2015 · 2015
A deep dive into word sense disambiguation with LSTM
Minh Le, Marten Postma, Jacopo Urbani, and Piek Vossen. 2018 · 2018
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UMAP: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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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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Embeddings for word sense disambiguation: An evaluation study
Ignacio Iacobacci, Mohammad Taher Pilehvar, and Roberto Navigli. 2016 · 2016
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Semi-supervised word sense disambiguation with neural models
Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, and Eric Altendorf. 2016 · 2016
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Tying word vectors and word classifiers: A loss framework for language modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher. 2017 · 2017
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Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
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Word sense disambiguation: A unified evaluation framework and empirical comparison
Alessandro Raganato, Jose Camacho-Collados, and Roberto Navigli. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
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Language modelling makes sense: Propagating representations through WordNet for full-coverage word sense disambiguation
Daniel Loureiro and Alípio Jorge. 2019 · 2019
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Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Visualizing and measuring the geometry of BERT
Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B Viegas, Andy Coenen, Adam Pearce, and Been Kim. 2019 · 2019
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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