klinger.bib

@proceedings{wassa-2023-approaches,
  title = {Proceedings of the 13th Workshop on Computational
                  Approaches to Subjectivity, Sentiment, {\&} Social
                  Media Analysis},
  editor = {Barnes, Jeremy and De Clercq, Orph{\'e}e and
                  Klinger, Roman},
  month = jul,
  year = {2023},
  address = {Toronto, Canada},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.wassa-1.0},
  internaltype = {edited}
}
@inproceedings{Klinger2023a,
  author = {Roman Klinger},
  title = {Where are We in Event-centric Emotion Analysis?
                  Bridging Emotion Role Labeling and Appraisal-based
                  Approaches},
  booktitle = {Proceedings of the Big Picture Workshop: Crafting a
                  Research Narrative},
  year = {2023},
  month = {December},
  address = {Singapore},
  organization = {EMNLP},
  publisher = {Association for Computational Linguistics},
  url = {https://www.romanklinger.de/publications/klinger2023.pdf},
  archiveprefix = {arXiv},
  eprint = {2309.02092},
  primaryclass = {cs.CL},
  internaltype = {workshop}
}
@article{Velutharambath2023a,
  author = {Aswathy Velutharambath and Kai Sassenberg and Roman Klinger},
  title = {Prevention or Promotion? {Predicting} Author's Regulatory Focus},
  journal = {Northern European Journal of Language Technology},
  year = {2023},
  volume = {9},
  number = {1},
  month = {September},
  internaltype = {journal},
  url = {https://www.romanklinger.de/publications/VelutharambathSassenbergKlinger_NEJLT2023.pdf},
  doi = {10.3384/nejlt.2000-1533.2023.4561}
}
@article{Troiano2023a,
  author = {Enrica Troiano and Roman Klinger and Sebastian Padó},
  title = {On the Relationship between Frames and Emotionality in Text},
  journal = {Northern European Journal of Language Technology},
  year = {2023},
  volume = {9},
  number = {1},
  month = {September},
  internaltype = {journal},
  doi = {10.3384/nejlt.2000-1533.2023.4361},
  url = {https://www.romanklinger.de/publications/TroianoKlingerPado_nejlt23.pdf}
}
@inproceedings{MenchacaResendiz2023,
  title = {Affective Natural Language Generation of Event
                  Descriptions through Fine-grained Appraisal
                  Conditions},
  author = {Menchaca Resendiz, Yarik and Klinger, Roman},
  booktitle = {Proceedings of the 16th International Conference on
                  Natural Language Generation},
  month = sep,
  year = {2023},
  address = {Prague, Czech Republic},
  publisher = {Association for Computational Linguistics},
  internaltype = {conferenceproc},
  archiveprefix = {arXiv},
  eprint = {2307.14004},
  url = {https://aclanthology.org/2023.inlg-1.26},
  pdf = {https://www.romanklinger.de/publications/MenchacaResendiz_Klinger_INLG2023.pdf}
}
@inproceedings{MenchacaResendiz2023b,
  title = {Emotion-Conditioned Text Generation through
                  Automatic Prompt Optimization},
  author = {Menchaca Resendiz, Yarik and Klinger, Roman},
  booktitle = {Proceedings of the 1st Workshop on Taming Large
                  Language Models: Controllability in the era of
                  Interactive Assistants!},
  month = sep,
  year = 2023,
  address = {Prague, Czech Republic},
  publisher = {Association for Computational Linguistics},
  internaltype = {workshop},
  archiveprefix = {arXiv},
  eprint = {2308.04857},
  pdf = {https://www.romanklinger.de/publications/MenchacaResendiz_Klinger_TLLM2023.pdf}
}
@inproceedings{Wegge2023,
  author = {Maximilian Wegge and Roman Klinger},
  title = {Automatic Emotion Experiencer Recognition},
  booktitle = {3rd Workshop on Computational Linguistics for the
                  Political and Social Sciences (CPSS)},
  year = 2023,
  month = may,
  pdf = {https://www.romanklinger.de/publications/WeggeKlinger2023.pdf},
  internaltype = {workshop},
  archiveprefix = {arXiv},
  eprint = {2305.16731}
}
@inproceedings{Velutharambath2023,
  title = {{UNIDECOR}: A Unified Deception Corpus for
                  Cross-Corpus Deception Detection},
  author = {Velutharambath, Aswathy and Klinger, Roman},
  booktitle = {Proceedings of the 13th Workshop on Computational
                  Approaches to Subjectivity, Sentiment, {\&} Social
                  Media Analysis},
  month = jul,
  year = {2023},
  address = {Toronto, Canada},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.wassa-1.5},
  pages = {39--51},
  abstract = {Verbal deception has been studied in psychology,
                  forensics, and computational linguistics for a
                  variety of reasons, like understanding behaviour
                  patterns, identifying false testimonies, and
                  detecting deception in online communication. Varying
                  motivations across research fields lead to
                  differences in the domain choices to study and in
                  the conceptualization of deception, making it hard
                  to compare models and build robust deception
                  detection systems for a given language. With this
                  paper, we improve this situation by surveying
                  available English deception datasets which include
                  domains like social media reviews, court
                  testimonials, opinion statements on specific topics,
                  and deceptive dialogues from online strategy
                  games. We consolidate these datasets into a single
                  unified corpus. Based on this resource, we conduct a
                  correlation analysis of linguistic cues of deception
                  across datasets to understand the differences and
                  perform cross-corpus modeling experiments which show
                  that a cross-domain generalization is challenging to
                  achieve. The unified deception corpus (UNIDECOR) can
                  be obtained from
                  https://www.ims.uni-stuttgart.de/data/unidecor.},
  internaltype = {workshop},
  pdf = {https://www.romanklinger.de/publications/VelutharambathKlinger_UNIDECOR_WASSA2023.pdf},
  archiveprefix = {arXiv},
  eprint = {2306.02827}
}
@incollection{Klinger2023,
  address = {Berlin, Heidelberg},
  title = {Emotionsklassifikation in {Texten} unter
                  {Berücksichtigung} des {Komponentenprozessmodells}},
  isbn = {978-3-662-65963-2 978-3-662-65964-9},
  url = {https://link.springer.com/10.1007/978-3-662-65964-9_7},
  abstract = {Zusammenfassung Ein wichtiger Bestandteil unserer
                  alltäglichen Kommunikation, neben der Mitteilung und
                  Beschreibung von Ereignissen und Fakten, ist der
                  Ausdruck von Emotionen, welcher auch Bestandteil von
                  Hassrede ist: Es wird zum Beispiel Wut zum Ausdruck
                  gebracht, was wiederum bei den Betroffenen Angst,
                  Traurigkeit oder vielleicht auch Überraschung
                  auslösen kann. In der maschinellen Verarbeitung von
                  Sprache haben sich in der letzten Zeit einige
                  konkrete Aufgaben, welche Teil der Emotionsanalyse
                  in Text sind, herauskristallisiert. Diese sind zum
                  einen Klassifikationsaufgaben (welche Emotion drückt
                  ein Text aus?) und zum anderen relationale
                  Strukturlernaufgaben (welche Wörter bezeichnen die
                  Person, die eine Emotion fühlt und welche Wörter
                  lassen auf die Ursache der Emotion schließen?). Wir
                  verschaffen uns in diesem Kapitel einen kurzen
                  Überblick über das Feld und diskutieren im Anschluss
                  etwas genauer, wie sich die Beschreibungen von
                  Emotionen in verschiedenen Domänen unterscheiden und
                  wie Ereignisbeschreibungen mit Hilfe psychologischer
                  Theorien mit Emotionen zusammengebracht werden
                  können. Insbesondere analysieren wir auf Basis des
                  Emotions-Komponenten-Prozessmodells, auf welche
                  Komponenten von Emotionen (subjektives Gefühl,
                  kognitive Evaluation, Körperreaktion, Ausdruck,
                  Motivation) Autor:innen zugreifen, und stellen fest,
                  dass diese Verteilung zwischen sozialen Medien und
                  Literatur unterschiedlich ist. In beiden Domänen
                  spielt aber die kognitive Komponente zur
                  Interpretation von Emotionen eine wichtige
                  Rolle. Dies zeigt auf, dass insbesondere der
                  Ereignisinterpretation Aufmerksamkeit geschenkt
                  werden muss, um implizit kommunizierte Emotionen
                  aufzudecken. Dies motiviert uns, Emotionen mit Hilfe
                  der Appraisaltheorien zu analysieren, welche den
                  Zusammenhang zwischen kognitiven Prozessen und
                  Emotionen erklären. Zu beiden Konzepten – dem
                  Komponentenmodell und den Appraisaltheorien –
                  präsentieren wir Textkorpora und
                  Klassifikationsmodelle.},
  language = {de},
  booktitle = {Digitale {Hate} {Speech}},
  publisher = {Springer Berlin Heidelberg},
  author = {Klinger, Roman},
  editor = {Jaki, Sylvia and Steiger, Stefan},
  year = {2023},
  doi = {10.1007/978-3-662-65964-9_7},
  pages = {131--154},
  internaltype = {conferenceproc}
}
@inproceedings{StajnerKlinger2023,
  title = {Emotion Analysis from Texts},
  author = {\v{S}tajner, Sanja and Klinger, Roman},
  booktitle = {Proceedings of the 17th Conference of the European
                  Chapter of the Association for Computational
                  Linguistics: Tutorial Abstracts},
  month = may,
  year = {2023},
  address = {Dubrovnik, Croatia},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.eacl-tutorials.2},
  pages = {7--12},
  abstract = {Emotion analysis in text is an area of research that
                  encompasses a set of various natural language
                  processing (NLP) tasks, including classification and
                  regression settings, as well as structured
                  prediction tasks like role labelling or stimulus
                  detection. In this tutorial, we provide an overview
                  of research from emotion psychology which sets the
                  ground for choosing adequate NLP methodology, and
                  present existing resources and classification
                  methods used for emotion analysis in texts. We
                  further discuss appraisal theories and how events
                  can be interpreted regarding their presumably caused
                  emotion and briefly introduce emotion role
                  labelling. In addition to these technical topics, we
                  discuss the use cases of emotion analysis in text,
                  their societal impact, ethical considerations, as
                  well as the main challenges in the field.},
  pdf = {https://eacl2023tutorial.github.io/EmotionAnalysis-EACL-Tutorial-Summary.pdf},
  internaltype = {abstrconf}
}
@inproceedings{Wuehrl2023,
  author = {Amelie W\"uhrl and Lara Grimminger and Roman
                  Klinger},
  title = {An Entity-based Claim Extraction Pipeline for
                  Real-world Fact-checking},
  month = {May},
  year = {2023},
  booktitle = {Proceedings of the Sixth Fact Extraction and
                  VERification Workshop (FEVER)},
  address = {Dubrovnik, Croatia},
  organization = {Association for Computational Linguistics},
  pdf = {https://www.romanklinger.de/publications/WuehrlKlingerFEVER2023.pdf},
  url = {https://aclanthology.org/2023.fever-1.3},
  pages = {29–37},
  internaltype = {workshop},
  archiveprefix = {arXiv},
  eprint = {2304.05268}
}
@article{Troiano2023,
  author = {Enrica Troiano and Laura Oberl\"ander and Roman
                  Klinger},
  title = {Dimensional Modeling of Emotions in Text with
                  Appraisal Theories: Corpus Creation, Annotation
                  Reliability, and Prediction},
  journal = {Computational Linguistics},
  number = 1,
  volume = 49,
  month = mar,
  year = 2023,
  address = {Cambridge, MA},
  publisher = {MIT Press},
  abstract = {The most prominent tasks in emotion analysis are to
                  assign emotions to texts and to understand how
                  emotions manifest in language. An observation for
                  NLP is that emotions can be communicated implicitly
                  by referring to events, appealing to an empathetic,
                  intersubjective understanding of events, even
                  without explicitly mentioning an emotion name. In
                  psychology, the class of emotion theories known as
                  appraisal theories aims at explaining the link
                  between events and emotions. Appraisals can be
                  formalized as variables that measure a cognitive
                  evaluation by people living through an event that
                  they consider relevant. They include the assessment
                  if an event is novel, if the person considers
                  themselves to be responsible, if it is in line with
                  the own goals, and many others. Such appraisals
                  explain which emotions are developed based on an
                  event, e.g., that a novel situation can induce
                  surprise or one with uncertain consequences could
                  evoke fear. We analyze the suitability of appraisal
                  theories for emotion analysis in text with the goal
                  of understanding if appraisal concepts can reliably
                  be reconstructed by annotators, if they can be
                  predicted by text classifiers, and if appraisal
                  concepts help to identify emotion categories. To
                  achieve that, we compile a corpus by asking people
                  to textually describe events that triggered
                  particular emotions and to disclose their
                  appraisals. Then, we ask readers to reconstruct
                  emotions and appraisals from the text. This setup
                  allows us to measure if emotions and appraisals can
                  be recovered purely from text and provides a human
                  baseline. Our comparison of text classification
                  methods to human annotators shows that both can
                  reliably detect emotions and appraisals with similar
                  performance. Therefore, appraisals constitute an
                  alternative computational emotion analysis paradigm
                  and further improve the categorization of emotions
                  in text with joint models.},
  doi = {10.1162/coli_a_00461},
  url = {https://doi.org/10.1162/coli_a_00461},
  internaltype = {journal},
  archiveprefix = {arXiv},
  eprint = {2206.05238}
}