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Information Systems and Applied Computer Sciences

Fundamentals of Natural Language Processing

  1.  University of bamberg
  2. Faculties
  3. Information Systems and Applied Computer Sciences
  4. Subject groups
  5. Applied Computer Science
  6. Fundamentals of Natural Language Processing
  7. Team
  8. Prof. Dr. Roman Klinger

Prof. Dr. Roman Klinger

Prof. Dr. Roman Klinger heads the BamNLP research group and is professor for  Fundamentals of Natural Language Processing.

He studied computer science with a minor in psychology, holds a Ph.D. in computer science from TU Dortmund University (2011), and received the venia legendi in computer science in Stuttgart (2020). Before moving to Bamberg, he worked at the Institute for Natural Language Processing in Stuttgart, at the University of Bielefeld, at the Fraunhofer Institute for Algorithms and Scientific Computing, and the University of Massachusetts Amherst. Roman Klinger’s vision is to enable computers to understand and generate text regarding both factual and non-factual information. This finds application in interdisciplinary research, including biomedical text mining, digital humanities, modelling psychological concepts (like emotions) in language, and social media mining. These topics often constitute novel challenges to existing machine learning methods. Therefore, he and his group also contribute to the fields of probabilistic and deep machine learning.

You can find more detailed information on Roman Klinger at https://romanklinger.de/cv/.

Selected Publications

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.