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Language Models are the core for almost any Natural Language Processing system nowadays.
Using a semantic concordance for sense identification
George A. Miller, Martin Chodorow, Shari Landes, Claudia Leacock, and Robert G. Thomas. 1994 · 1994
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WordNet: An Electronic Lexical Database
Christiane Fellbaum, editor. 1998 · 1998
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SENSEVAL-2: Overview
Philip Edmonds and Scott Cotton. 2001 · 2001
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The role of domain information in word sense disambiguation
Bernardo Magnini, Carlo Strapparava, Giovanni Pezzulo, and Alfio Gliozzo. 2002 · 2002
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Revising the Wordnet domains hierarchy: semantics, coverage and balancing
Luisa Bentivogli, Pamela Forner, Bernardo Magnini, and Emanuele Pianta. 2004 · 2004
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The English all-words task
Benjamin Snyder and Martha Palmer. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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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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It makes sense: A wide-coverage word sense disambiguation system for free text
Zhi Zhong and Hwee Tou Ng. 2010 · 2010
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A graph-based method to improve wordnet domains
Aitor González, German Rigau, and Mauro Castillo. 2012 · 2012
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A proposal for improving WordNet domains
Aitor González-Agirre, Mauro Castillo, and German Rigau. 2012 · 2012
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Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network
Roberto Navigli and Simone Ponzetto. 2012 · 2012
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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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Random walks for knowledge-based word sense disambiguation
Eneko Agirre, Oier López de Lacalle, and Aitor Soroa. 2014 · 2014
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Entity linking meets word sense disambiguation: a unified approach
Andrea Moro, Alessandro Raganato, and Roberto Navigli. 2014 · 2014
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A large-scale pseudoword-based evaluation framework for state-of-the-art word sense disambiguation
Mohammad Taher Pilehvar and Roberto Navigli. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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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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BabelDomains: Large-scale domain labeling of lexical resources
Jose Camacho-Collados and Roberto Navigli. 2017 · 2017
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Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
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Neural sequence learning models for word sense disambiguation
Alessandro Raganato, Claudio Delli Bovi, and Roberto Navigli. 2017 · 2017
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Inference is everything: Recasting semantic resources into a unified evaluation framework
Aaron Steven White, Pushpendre Rastogi, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Collecting diverse natural language inference problems for sentence representation evaluation
Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, and Benjamin Van Durme. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Adapting BERT for word sense disambiguation with gloss selection objective and example sentences
Boon Peng Yap, Andrew Koh, and Eng Siong Chng. 2020 · 2020
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Universal natural language processing with limited annotations: Try few-shot textual entailment as a start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2020
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ESC: Redesigning WSD with extractive sense comprehension
Edoardo Barba, Tommaso Pasini, and Roberto Navigli. 2021 · 2021
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Recent trends in word sense disambiguation: A survey
Michele Bevilacqua, Tommaso Pasini, Alessandro Raganato, and Roberto Navigli. 2021 · 2021
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Quasi bidirectional encoder representations from transformers for word sense disambiguation
Michele Bevilacqua and Roberto Navigli. 2019 · 2019
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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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Improved word sense disambiguation using pre-trained contextualized word representations
Christian Hadiwinoto, Hwee Tou Ng, and Wee Chung Gan. 2019 · 2019
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GlossBERT: BERT for word sense disambiguation with gloss knowledge
Luyao Huang, Chi Sun, Xipeng Qiu, and Xuanjing Huang. 2019 · 2019
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Zero-shot word sense disambiguation using sense definition embeddings
Sawan Kumar, Sharmistha Jat, Karan Saxena, and Partha Talukdar. 2019 · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 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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Framing word sense disambiguation as a multi-label problem for model-agnostic knowledge integration
Simone Conia and Roberto Navigli. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
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Zero-shot event extraction via transfer learning: Challenges and insights
Qing Lyu, Hongming Zhang, Elior Sulem, and Dan Roth. 2021 · 2021
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, and Dan Roth. 2021 · 2021
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Label verbalization and entailment for effective zero and few-shot relation extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021 · 2021
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Ask2Transformers: Zero-shot domain labelling with pretrained language models
Oscar Sainz and German Rigau. 2021 · 2021
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Few-shot text generation with natural language instructions
Timo Schick and Hinrich Schütze. 2021b · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021c · 2021
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Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. 2021 · 2021
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Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip Yu. 2021 · 2021
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Probing via prompting
Jiaoda Li, Ryan Cotterell, and Mrinmaya Sachan. 2022b · 2022
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Textual entailment for event argument extraction: Zero- and few-shot with multi-source learning
Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, and Eneko Agirre. 2022a · 2022
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ZS4IE: A toolkit for zero-shot information extraction with simple verbalizations
Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, and Bonan Min. 2022b · 2022
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Zero-shot aspect-based sentiment analysis
Lei Shu, Hu Xu, Bing Liu, and Jiahua Chen. 2022 · 2022
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SEE-few: Seed, expand and entail for few-shot named entity recognition
Zeng Yang, Linhai Zhang, and Deyu Zhou. 2022 · 2022
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