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WASSA 2021 Shared Task: Predicting Empathy and Emotion in Reaction to News Stories., , , , , and . WASSA@EACL, page 92-104. Association for Computational Linguistics, (2021)Towards Label-Agnostic Emotion Embeddings., , and . EMNLP (1), page 9231-9249. Association for Computational Linguistics, (2021)Towards a Metric for Paraphrastic Modification., , and . DH, page 457-459. Red de Humanidades Digitales A. C., (2018)"Romantik" im aktuellen parteipolitischen Diskurs auf Twitter., , and . DHd, (2020)Proceedings of the 11th Linguistic Annotation Workshop, and . chapter Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation, page 1--12. Association for Computational Linguistics, (2017)Learning Word Ratings for Empathy and Distress from Document-Level User Responses., , , , and . CoRR, (2019)Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection., , and . LaTeCH@NAACL-HLT, page 1-11. Association for Computational Linguistics, (2019)Learning and Evaluating Emotion Lexicons for 91 Languages., , and . ACL, page 1202-1217. Association for Computational Linguistics, (2020)Modeling Empathy and Distress in Reaction to News Stories., , , , and . EMNLP, page 4758-4765. Association for Computational Linguistics, (2018)Learning Neural Emotion Analysis from 100 Observations: The Surprising Effectiveness of Pre-Trained Word Representations., , , and . CoRR, (2018)