Prof. Dr. Roman Klinger
Prof. Dr. Roman Klinger leitet die BamNLP Arbeitsgruppe und ist Professor für Grundlagen der Sprachverarbeitung.
Er studierte Informatik mit Nebenfach 球探足球比分, promovierte in Informatik an der TU Dortmund (2011) und erhielt die Venia Legendi in Informatik in Stuttgart (2020). Bevor er nach Bamberg kam, arbeitete er am Institut für Maschinelle Sprachverarbeitung in Stuttgart, an der Universit?t Bielefeld, am Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen und an der University of Massachusetts Amherst. Roman Klingers Vision ist es, Computer in die Lage zu versetzen, Texte zu verstehen und zu generieren, die sowohl faktische als auch nicht-faktische Informationen enthalten. Dies findet Anwendung in der interdisziplin?ren Forschung, einschlie?lich biomedizinischem Text Mining, digitalen Geisteswissenschaften, Modellierung psychologischer Konzepte (wie Emotionen) in der Sprache und Social Media Mining. Diese Themen stellen oft neue Herausforderungen für bestehende Methoden des maschinellen Lernens dar. Daher tragen er und seine Gruppe auch zu den Bereichen probabilistisches und tiefes maschinelles Lernen bei.
Detaillierte Informationen zu Herrn Klingers Lebenslauf finden Sie auf seiner Internetseite https://romanklinger.de/cv/.
Ausgew?hlte Publikationen
Bagdon, Christopher Doyle et al. (2024): “You are an expert annotator”: Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Mexico City, Mexico: Association for Computational Linguistics. S. 7917–7929.
Troiano, Enrica et al. (2024): Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Miami, Florida: Association for Computational Linguistics. S. 1634–1658.
Wegge, Maximilian/Klinger, Roman (2024): Topic Bias in Emotion Classification. In: Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024). San ?iljan, Malta: Association for Computational Linguistics. S. 89–103.
Wührl, Amelie et al. (2024): What Makes Medical Claims (Un)Verifiable?: Analyzing Entity and Relation Properties for Fact Verification. In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. S. 2046–2058.
Klinger, Roman (2023): Where are We in Event-centric Emotion Analysis?: Bridging Emotion Role Labeling and Appraisal-based Approaches. In: Proceedings of the Big Picture Workshop. Singapore: Association for Computational Linguistics. S. 1–17.
Menchaca Resendiz, Yarik/Klinger, Roman (2023): Emotion-Conditioned Text Generation through Automatic Prompt Optimization. In: Proceedings of the 1st Workshop on Taming Large Language Models: Controllability in the era of Interactive Assistants! Prag: Association for Computational Linguistics. S. 24–30.
Troiano, Enrica/Oberl?nder, Laura/Klinger, Roman (2023): Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction. In: Computational linguistics 49, S. 1–72.
Velutharambath, Aswathy/Sassenberg, Kai/Klinger, Roman (2023): Prevention or Promotion?: Predicting Author’s Regulatory Focus. In: Northern European Journal of Language Technology 9.
Papay, Sean/Klinger, Roman/Padó, Sebastian (2022): Constraining Linear-chain CRFs to Regular Languages. arxiv.
Plaza-del-Arco, Flor Miriam/Martín-Valdivia, María-Teresa/Klinger, Roman (2022): Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora. In: Proceedings of the 29th International Conference on Computational Linguistics. Gyeongju: International Committee on Computational Linguistics. S. 6805–6817.
Sabbatino, Valentino et al. (2022): “splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words. In: Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis. Dublin: Association for Computational Linguistics. S. 37–50.
Troiano, Enrica/Velutharambath, Aswathy/Klinger, Roman (2022): From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey. In: Natural Language Engineering 29, S. 849–908.
Wührl, Amelie/Klinger, Roman (2022): Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR). In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. Marseille: European Language Resources Association. S. 4439–4450.
Grimminger, Lara/Klinger, Roman (2021): Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection. In: Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics. S. 171–180.
Troiano, Enrica/Padó, Sebastian/Klinger, Roman (2021): Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled. In: Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics. S. 40–49.
Bostan, Laura Ana Maria/Kim, Evgeny/Klinger, Roman (2020): GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception. In: Proceedings of the Twelfth Language Resources and Evaluation Conference. Paris: European Language Resources Association. S. 1554–1566.
Haider, Thomas et al. (2020): PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry. In: Proceedings of the Twelfth Language Resources and Evaluation Conference. Paris: European Language Resources Association. S. 1652–1663.
Papay, Sean/Klinger, Roman/Padó, Sebastian (2020): Dissecting Span Identification Tasks with Performance Prediction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. S. 4881–4895.
Troiano, Enrica/Klinger, Roman/Padó, Sebastian (2020): Lost in Back-Translation: Emotion Preservation in Neural Machine Translation. In: Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics. S. 4340–4354.
Kim, Evgeny/Klinger, Roman (2019): Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. S. 647–653.
Troiano, Enrica/Klinger, Roman/Padó, Sebastian (2019): Crowdsourcing and Validating Event-focused Emotion Corpora for German and English. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. S. 4005–4011.
Kim, Evgeny/Klinger, Roman (2018): Who Feels What and Why?: Annotation of a Literature Corpus with Semantic Roles of Emotions. In: Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics. S. 1345–1359.