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The experimental landscape in natural language processing for social media is too fragmented.
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 · 1907
Earlier work this paper cites.
Semeval-2017 task 4: Sentiment analysis in twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2019 · 1912
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Visual attention model for name tagging in multimodal social media
Di Lu, Leonardo Neves, Vitor Carvalho, Ning Zhang, and Heng Ji. 2018 · 1999
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Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, and Rada Mihalcea. 2020 · 2005
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Lexical normalisation of short text messages: Makn sens a# twitter
Bo Han and Timothy Baldwin. 2011 · 2011
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Named entity recognition in tweets: an experimental study
Alan Ritter, Sam Clark, Oren Etzioni, et al. 2011 · 2011
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How noisy social media text, how diffrnt social media sources?
Timothy Baldwin, Paul Cook, Marco Lui, Andrew MacKinlay, and Li Wang. 2013 · 2013
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Twitter part-of-speech tagging for all: Overcoming sparse and noisy data
Leon Derczynski, Alan Ritter, Sam Clark, and Kalina Bontcheva. 2013 · 2013
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Dude, srsly?: The surprisingly formal nature of twitter’s language
Yuheng Hu, Kartik Talamadupula, and Subbarao Kambhampati. 2013 · 2013
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Code mixing: A challenge for language identification in the language of social media
Utsab Barman, Amitava Das, Joachim Wagner, and Jennifer Foster. 2014 · 2014
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Sociolinguistic analysis of twitter in multilingual societies
Suin Kim, Ingmar Weber, Li Wei, and Alice Oh. 2014 · 2014
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Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition
Timothy Baldwin, Marie-Catherine de Marneffe, Bo Han, Young-Bum Kim, Alan Ritter, and Wei Xu. 2015 · 2015
Cited alongside, same era.
Semeval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016 · 2016
Cited alongside, same era.
Detecting Sarcasm in Multimodal Social Platforms
Rossano Schifanella, Paloma de Juan, Joel Tetreault, and Liangliang Cao. 2016 · 2016
Cited alongside, same era.
En-es-cs: An english-spanish code-switching twitter corpus for multilingual sentiment analysis
David Vilares, Miguel A Alonso, and Carlos Gómez-Rodríguez. 2016 · 2016
Cited alongside, same era.
Generalisation in named entity recognition: A quantitative analysis
Isabelle Augenstein, Leon Derczynski, and Kalina Bontcheva. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Part-of-speech tagging for twitter: Annotation, features, and experiments
Kevin Gimpel, Nathan Schneider, Brendan O’Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith. 2018 · 2018
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Semeval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018 · 2018
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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Time Salimans, and Ilya Sutskever. 2018 · 2018
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Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018 · 2018
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
From shakespeare to twitter: What are language styles all about?
Wei Xu. 2017 · 2017
Cited alongside, same era.
Semeval 2018 task 2: Multilingual emoji prediction
Francesco Barbieri, Jose Camacho-Collados, Francesco Ronzano, Luis Espinosa Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, and Horacio Saggion. 2018 · 2018
Cited alongside, same era.
Tübingen-oslo at semeval-2018 task 2: Svms perform better than rnns in emoji prediction
Çağrı Çöltekin and Taraka Rama. 2018 · 2018
Cited alongside, same era.
SentEval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela. 2018 · 2018
Cited alongside, same era.
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 R. Bowman. 2019a
Cited in the paper.
SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019 · 2019
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
Later among the works it cites.
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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Detection of Abusive Language: the Problem of Biased Datasets
Michael Wiegand, Josef Ruppenhofer, and Thomas Kleinbauer. 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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SemEval-2019 task 6: Identifying and categorizing offensive language in social media (OffensEval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019 · 2019
Later among the works it cites.