SemEval-2018 Task 3: Irony Detection in English Tweets
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[1] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[2] Bing Liu,et al. Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.
[3] Finn Årup Nielsen,et al. A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.
[4] Yann LeCun,et al. Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..
[5] Jun Hong,et al. Sarcasm Detection on Czech and English Twitter , 2014, COLING.
[6] Diana Maynard,et al. Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.
[7] Pushpak Bhattacharyya,et al. Automatic Sarcasm Detection , 2016, ACM Comput. Surv..
[8] Raj Kumar Gupta,et al. CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification , 2017, *SEMEVAL.
[9] Chuhan Wu,et al. THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning , 2018, *SEMEVAL.
[10] Ari Rappoport,et al. Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.
[11] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[12] Anil Kumar Singh,et al. NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets , 2018, *SEMEVAL.
[13] Eric Gilbert,et al. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.
[14] Véronique Hoste,et al. Monday mornings are my fave : ) #not Exploring the Automatic Recognition of Irony in English tweets , 2016, COLING.
[15] Saif Mohammad,et al. Emotion Intensities in Tweets , 2017, *SEMEVAL.
[16] Antal van den Bosch,et al. Signaling sarcasm: From hyperbole to hashtag , 2015, Inf. Process. Manag..
[17] Byron C. Wallace,et al. Modelling Context with User Embeddings for Sarcasm Detection in Social Media , 2016, CoNLL.
[18] Horacio Saggion,et al. Modelling Irony in Twitter , 2014, EACL.
[19] David Bamman,et al. Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.
[20] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[21] Tomoaki Ohtsuki,et al. Sarcasm Detection in Twitter: "All Your Products Are Incredibly Amazing!!!" - Are They Really? , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).
[22] Nathalie Aussenac-Gilles,et al. Towards a Contextual Pragmatic Model to Detect Irony in Tweets , 2015, ACL.
[23] Pushpak Bhattacharyya,et al. Harnessing Cognitive Features for Sarcasm Detection , 2016, ACL.
[24] Paolo Rosso,et al. Irony Detection in Twitter , 2016, ACM Trans. Internet Techn..
[25] Els Lefever,et al. Guidelines for Annotating Irony in Social Media Text, version 2.0 , 2016 .
[26] Nina Wacholder,et al. Identifying Sarcasm in Twitter: A Closer Look , 2011, ACL.
[27] Robert L. Mercer,et al. Class-Based n-gram Models of Natural Language , 1992, CL.
[28] Tony Veale,et al. IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media , 2018, *SEMEVAL.
[29] Iyad Rahwan,et al. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm , 2017, EMNLP.
[30] Björn W. Schuller,et al. SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives , 2016, COLING.
[31] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[32] Tomas Mikolov,et al. Bag of Tricks for Efficient Text Classification , 2016, EACL.
[33] Dai Quoc Nguyen,et al. NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter , 2018, *SEMEVAL.
[34] Paolo Rosso,et al. A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.
[35] J. Fleiss. Measuring nominal scale agreement among many raters. , 1971 .
[36] Sampo Pyysalo,et al. brat: a Web-based Tool for NLP-Assisted Text Annotation , 2012, EACL.
[37] Anna Rumshisky,et al. SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor , 2017, *SEMEVAL.
[38] Preslav Nakov,et al. SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.
[39] Paolo Rosso,et al. SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.
[40] Byron C. Wallace. Computational irony: A survey and new perspectives , 2013, Artificial Intelligence Review.
[41] Tony Veale,et al. Fracking Sarcasm using Neural Network , 2016, WASSA@NAACL-HLT.
[42] Jean Carletta,et al. Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.
[43] Ellen Riloff,et al. Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.
[44] Richard Evans,et al. WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony , 2018, *SEMEVAL.
[45] Cynthia Van Hee. Can machines sense irony? : exploring automatic irony detection on social media , 2017 .
[46] Preslav Nakov,et al. SemEval-2014 Task 9: Sentiment Analysis in Twitter , 2014, *SEMEVAL.
[47] Veselin Stoyanov,et al. Evaluation Measures for the SemEval-2016 Task 4 “Sentiment Analysis in Twitter” (Draft: Version 1.13) , 2016 .
[48] C. Shelley. The bicoherence theory of situational irony , 2001 .