暂无分享,去创建一个
Vinodkumar Prabhakaran | Mark D'iaz | Aida Mostafazadeh Davani | Vinodkumar Prabhakaran | M. D'iaz | A. Davani
[1] R. Plutchik. A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION , 1980 .
[2] Brendan T. O'Connor,et al. Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.
[3] Alan S. Cowen,et al. GoEmotions: A Dataset of Fine-Grained Emotions , 2020, ACL.
[4] Bob Carpenter,et al. The Benefits of a Model of Annotation , 2013, Transactions of the Association for Computational Linguistics.
[5] Emily Denton,et al. Social Biases in NLP Models as Barriers for Persons with Disabilities , 2020, ACL.
[6] Klaus Krippendorff,et al. Agreement and Information in the Reliability of Coding , 2011 .
[7] Ingmar Weber,et al. Understanding Abuse: A Typology of Abusive Language Detection Subtasks , 2017, ALW@ACL.
[8] Eduard Hovy,et al. Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances , 2019, IEEE Access.
[9] Udo Kruschwitz,et al. Comparing Bayesian Models of Annotation , 2018, TACL.
[10] Zeerak Waseem,et al. Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter , 2016, NLP+CSS@EMNLP.
[11] Philipp Blandfort,et al. Annotating Social Media Data From Vulnerable Populations: Evaluating Disagreement Between Domain Experts and Graduate Student Annotators , 2019, HICSS.
[12] Alan S. Cowen,et al. Mapping the Passions: Toward a High-Dimensional Taxonomy of Emotional Experience and Expression , 2019, Psychological science in the public interest : a journal of the American Psychological Society.
[13] Margaret Mitchell,et al. Perturbation Sensitivity Analysis to Detect Unintended Model Biases , 2019, EMNLP.
[14] Walter Karlen,et al. CXPlain: Causal Explanations for Model Interpretation under Uncertainty , 2019, NeurIPS.
[15] Douwe Kiela,et al. Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection , 2021, Annual Meeting of the Association for Computational Linguistics.
[16] Yiwei Luo,et al. DeSMOG: Detecting Stance in Media On Global Warming , 2020, FINDINGS.
[17] Cecilia Ovesdotter Alm. Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications , 2011, ACL.
[18] Helen Yannakoudakis,et al. Tackling Online Abuse: A Survey of Automated Abuse Detection Methods , 2019, ArXiv.
[19] Bertie Vidgen,et al. Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020 , 2020, ALW.
[20] Jeffrey Heer,et al. Parting Crowds: Characterizing Divergent Interpretations in Crowdsourced Annotation Tasks , 2016, CSCW.
[21] Julia Hirschberg,et al. Experiments in Emotional Speech , 2003 .
[22] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[23] Aida Mostafazadeh Davani,et al. The Gab Hate Corpus: A collection of 27k posts annotated for hate speech , 2018 .
[24] Henry Lieberman,et al. A model of textual affect sensing using real-world knowledge , 2003, IUI '03.
[25] Margaret Lech,et al. Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[26] Yejin Choi,et al. Challenges in Automated Debiasing for Toxic Language Detection , 2021, EACL.
[27] Ge Jin,et al. Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating , 2020, LOUHI.
[28] Christopher Potts,et al. Did It Happen? The Pragmatic Complexity of Veridicality Assessment , 2012, CL.
[29] Yejin Choi,et al. The Risk of Racial Bias in Hate Speech Detection , 2019, ACL.
[30] Christine D. Piatko,et al. Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing , 2012, ExProM@ACL.
[31] Dirk Hovy,et al. Learning Whom to Trust with MACE , 2013, NAACL.
[32] Julia Hirschberg,et al. Detecting Hate Speech on the World Wide Web , 2012 .
[33] A. P. Dawid,et al. Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm , 1979 .
[34] Xiaodong Liu,et al. Multi-Task Deep Neural Networks for Natural Language Understanding , 2019, ACL.
[35] Carlo Strapparava,et al. SemEval-2007 Task 14: Affective Text , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).
[36] Hugo Liu,et al. A Corpus-based Approach to Finding Happiness , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.
[37] Anca Dumitrache. Crowdsourcing Disagreement for Collecting Semantic Annotation , 2015, ESWC.
[38] Bill Tomlinson,et al. Who are the crowdworkers?: shifting demographics in mechanical turk , 2010, CHI Extended Abstracts.
[39] Julia Hirschberg,et al. Classifying subject ratings of emotional speech using acoustic features , 2003, INTERSPEECH.
[40] Tamsyn P. Waterhouse,et al. Pay by the bit: an information-theoretic metric for collective human judgment , 2012, AAAI Fall Symposium: Machine Aggregation of Human Judgment.
[41] K. Fiedler,et al. Social Cognition: How Individuals Construct Social Reality , 2004 .
[42] David Jurgens,et al. A Just and Comprehensive Strategy for Using NLP to Address Online Abuse , 2019, ACL.
[43] Carlos Busso,et al. Interpreting ambiguous emotional expressions , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.
[44] Bo Pang,et al. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.
[45] Bing Liu,et al. Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.
[46] Véronique Hoste,et al. Emotion detection in suicide notes , 2013, Expert Syst. Appl..
[47] Janyce Wiebe,et al. Learning Subjective Language , 2004, CL.
[48] P. Ekman. An argument for basic emotions , 1992 .
[49] Kalina Bontcheva,et al. Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines , 2014, LREC.
[50] Dirk Hovy,et al. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter , 2016, NAACL.
[51] Stefanie Nowak,et al. How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation , 2010, MIR '10.
[52] Dirk Hovy,et al. Learning part-of-speech taggers with inter-annotator agreement loss , 2014, EACL.
[53] Lucas Dixon,et al. Six Attributes of Unhealthy Conversations , 2020, ALW.
[54] Tong Liu,et al. Human-in-the-Loop Learning From Crowdsourcing and Social Media , 2020 .
[55] Noel Crespi,et al. A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media , 2019, COMPLEX NETWORKS.
[56] Michael Wiegand,et al. A Survey on Hate Speech Detection using Natural Language Processing , 2017, SocialNLP@EACL.
[57] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[58] Andrés Montoyo,et al. Advances on natural language processing , 2007, Data Knowl. Eng..
[59] Cecilia Ovesdotter Alm,et al. Affect in Text and Speech , 2009 .
[60] Vinodkumar Prabhakaran,et al. On Releasing Annotator-Level Labels and Information in Datasets , 2021, LAW.
[61] J. Russell. Core affect and the psychological construction of emotion. , 2003, Psychological review.
[62] Michael Kläs,et al. Uncertainty in Machine Learning Applications: A Practice-Driven Classification of Uncertainty , 2018, SAFECOMP Workshops.
[63] Andrew Rosenberg,et al. "sure, I Did the Right Thing": a System for Sarcasm Detection in Speech , 2013, INTERSPEECH.