Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment

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Byron C. Wallace, Do Kook Choe, Eugene Charniak. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

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Authors

Byron Wallace

Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento

Do Kook Choe

John Brown University

Eugene Charniak

University of Geneva

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The University of Texas at Austin

John Brown University

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Wallace et al. (Thu,) studied this question.

synapsesocial.com/papers/6a11cbac11d1a9d8e25631f7 — DOI: https://doi.org/10.3115/v1/p15-1100

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Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)· 2014 · 134 citations
Sarcasm as Contrast between a Positive Sentiment and Negative Situation· 2013 · 590 citations
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing· 2012 · 181 citations
Sparse Additive Generative Models of Text· 2018 · 289 citations
Discovering Sociolinguistic Associations with Structured Sparsity· 2018 · 123 citations

Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)· 2014 · 134 citations
Sarcasm as Contrast between a Positive Sentiment and Negative Situation· 2013 · 590 citations
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing· 2012 · 181 citations
Sparse Additive Generative Models of Text· 2018 · 289 citations
Discovering Sociolinguistic Associations with Structured Sparsity· 2018 · 123 citations

Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment

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