This image shows Roman Klinger

Roman Klinger

Prof. Dr.

Adjunct Professor
Institute for Natural Language Processing (IMS)
Theoretische Computerlinguistik

Contact

Pfaffenwaldring 5 b
70569 Stuttgart
Deutschland
Room: 01.007

Office Hours

Only on appointment. For very short appointments, send a mail at least two days in advance. For longer appointments, plan with approximately two weeks leadtime.

Publikationen:
  1. 2023

    1. Klinger, R. (2023). Emotionsklassifikation in Texten unter                  Berücksichtigung des Komponentenprozessmodells. In S. Jaki & S. Steiger (Eds.), Digitale Hate Speech (pp. 131--154). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-65964-9_7
    2. Wührl, A., Grimminger, L., & Klinger, R. (2023). An Entity-based Claim Extraction Pipeline for                  Real-world Fact-checking. Proceedings of the Sixth Fact Extraction and                  VERification Workshop (FEVER).
    3. Stajner, S., & Klinger, R. (2023, May). Emotion Analysis in Text. Proceedings of the 17th Conference of the European                  Chapter of the Association for Computational                  Linguistics: Tutorial Abstracts.
    4. Velutharambath, A., & Klinger, R. (2023). UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection. Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, 39--51. https://aclanthology.org/2023.wassa-1.5
    5. Menchaca Resendiz, Y., & Klinger, R. (2023, September). Affective Natural Language Generation of Event                  Descriptions through Fine-grained Appraisal                  Conditions. Proceedings of the 16th International Conference on                  Natural Language Generation. https://arxiv.org/abs/2307.14004
    6. Klinger, R. (2023). Bridging Emotion Role Labeling and Appraisal-based Emotion Analysis. ArXiv E-Prints.
    7. Menchaca Resendiz, Y., & Klinger, R. (2023, September). Emotion-Conditioned Text Generation through                  Automatic Prompt Optimization. Proceedings of the 1st Workshop on Taming Large                  Language Models: Controllability in the Era of                  Interactive Assistants!
    8. Stajner, S., & Klinger, R. (2023). Emotion Analysis from Texts. Proceedings of the 17th Conference of the European                  Chapter of the Association for Computational                  Linguistics: Tutorial Abstracts, 7--12. https://aclanthology.org/2023.eacl-tutorials.2
    9. Troiano, E., Oberländer, L., & Klinger, R. (2023). Dimensional Modeling of Emotions in Text with                  Appraisal Theories: Corpus Creation, Annotation                  Reliability, and Prediction. Computational Linguistics, 49(1), Article 1. https://doi.org/10.1162/coli_a_00461
    10. Wegge, M., & Klinger, R. (2023, May). Automatic Emotion Experiencer Recognition. 3rd Workshop on Computational Linguistics for the Political and Social Sciences (CPSS).
    11. Velutharambath, A., Sassenberg, K., & Klinger, R. (2023). Prevention or Promotion? Predicting Author’s Regulatory Focus. Northern European Journal of Language Technology.
  2. 2022

    1. Mohr, I., Wührl, A., & Klinger, R. (2022, June). CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets. Proceedings of The 13th Language Resources and Evaluation Conference.
    2. Papay, S., Klinger, R., & Pado, S. (2022). Constraining Linear-chain CRFs to Regular Languages. International Conference on Learning Representations. https://openreview.net/forum?id=jbrgwbv8nD
    3. Wührl, A., & Klinger, R. (2022). Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR). Proceedings of the Language Resources and Evaluation Conference, 4439--4450. https://aclanthology.org/2022.lrec-1.472
    4. Wührl, A., & Klinger, R. (2022). Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets. Proceedings of the 9th Workshop on Argument Mining, 187--198. https://aclanthology.org/2022.argmining-1.18
    5. Troiano, E., Oberlaender, L. A. M., Wegge, M., & Klinger, R. (2022). x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations. Proceedings of the Language Resources and Evaluation Conference, 1365--1375. https://aclanthology.org/2022.lrec-1.146
    6. Kadikis, E., Srivastav, V., & Klinger, R. (2022). Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 6031--6037. https://aclanthology.org/2022.naacl-main.441
    7. Wegge, M., Troiano, E., Oberländer, L., & Klinger, R. (2022, December). Experiencer-Specific Emotion and Appraisal Prediction. Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social                  Science. https://arxiv.org/abs/2210.12078
    8. Khlyzova, A., Silberer, C., & Klinger, R. (2022). On the Complementarity of Images and Text for the Expression of Emotions in Social Media. Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 1--15. https://aclanthology.org/2022.wassa-1.1
    9. Plaza-del Arco, F. M., Mart\’ın-Valdivia, M.-T., & Klinger, R. (2022). Natural Language Inference Prompts for Zero-shot                  Emotion Classification in Text across Corpora. Proceedings of the 29th International Conference on                  Computational Linguistics, 6805--6817. https://aclanthology.org/2022.coling-1.592
    10. Barnes, J., De Clercq, O., Barriere, V., Tafreshi, S., Alqahtani, S., Sedoc, J., Klinger, R., & Balahur, A. (Eds.). (2022). Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis. Association for Computational Linguistics. https://aclanthology.org/2022.wassa-1.0
    11. Kreuter, A., Sassenberg, K., & Klinger, R. (2022). Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users. Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 315--323. https://aclanthology.org/2022.wassa-1.35
    12. Troiano, E., Velutharambath, A., & Klinger, R. (2022). From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey. Natural Language Engineering, 1–60. https://doi.org/10.1017/S1351324922000407
    13. Sabbatino, V., Troiano, E., Schweitzer, A., & Klinger, R. (2022). ``splink’’ is happy and ``phrouth’’ is scary: Emotion Intensity Analysis for Nonsense Words. Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 37--50. https://aclanthology.org/2022.wassa-1.4
  3. 2021

    1. Troiano, E., Padó, S., & Klinger, R. (2021). Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled. Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. https://www.aclweb.org/anthology/2021.wassa-1.5/
    2. Wührl, A., & Klinger, R. (2021). Claim Detection in Biomedical Twitter Posts. BioNLP: Proceedings of the 2021 Workshop on Biomedical Natural Language Processing. https://www.romanklinger.de/publications/WuehrlKlinger_BioNLP2021.pdf
    3. Casel, F., Heindl, A., & Klinger, R. (2021). Emotion Recognition under Consideration of the Emotion Component Process Model. Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), 49--61. https://aclanthology.org/2021.konvens-1.5
    4. Grimminger, L., & Klinger, R. (2021). Hate Towards the Political Opponent: A Twitter                  Corpus Study of the 2020 US Elections on the Basis                  of Offensive Speech and Stance Detection. Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. https://www.aclweb.org/anthology/2021.wassa-1.18/
    5. Dang, B. M. D., Oberländer, L., & Klinger, R. (2021). Emotion Stimulus Detection in German News Headlines. Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), 73--85. https://aclanthology.org/2021.konvens-1.7
    6. Plaza-del Arco, F. M., Halat, S., Padó, S., & Klinger, R. (2021). Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language. Forum for Information Retrieval Evaluation. https://arxiv.org/abs/2109.10255
    7. Hofmann, J., Troiano, E., & Klinger, R. (2021). Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations. Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. https://www.aclweb.org/anthology/2021.wassa-1.17
  4. 2020

    1. Oberländer, L., & Klinger, R. (2020). Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection. Proceedings of the 9th Joint Conferene on Lexical and Computational Semantics. https://arxiv.org/pdf/2010.07557.pdf
    2. Helbig, D., Troiano, E., & Klinger, R. (2020). Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline. Proceedings of the International Workshop on Natural Language Processing for Social Media (SocialNLP). https://doi.org/10.18653/v1/2020.socialnlp-1.6
    3. Bostan, L. A. M., Kim, E., & Klinger, R. (2020). GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC’20). European Language Resources Association (ELRA). https://www.aclweb.org/anthology/2020.lrec-1.194
    4. Klinger, R. (2020). Strukturierte Modellierung von Affekt in Text [Habilitation, University of Stuttgart]. http://romanklinger.de/publications/habilitation/Klinger-Affekt-in-Text-Habil-2020.pdf
    5. Haider, T., Eger, S., Kim, E., Klinger, R., & Menninghaus, W. (2020). PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC’20). European Language Resources Association (ELRA). https://www.aclweb.org/anthology/2020.lrec-1.205
    6. Sabbatino, V., Bostan, L. A. M., & Klinger, R. (2020). Automatic Section Recognition in Obituaries. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC’20). European Language Resources Association (ELRA). https://aclanthology.org/2020.lrec-1.102/
    7. Papay, S., Klinger, R., & Padó, S. (2020). Dissecting Span Identification Tasks with Performance Prediction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. http://www.romanklinger.de/publications/PapayKlingerPado2020.pdf
    8. Klinger, R., Kim, E., & Padó, S. (2020). Emotion Analysis for Literary Studies. In N. Reiter, A. Pichler, & J. Kuhn (Eds.), Reflektierte Algorithmische Textanalyse. De Gruyter.
    9. Hofmann, J., Troiano, E., Sassenberg, K., & Klinger, R. (2020). Appraisal Theories for Emotion Classification in Text. Proceedings of the 28th International Conference on Computational Linguistics. https://arxiv.org/abs/2003.14155
    10. Oberländer, L., Reich, K., & Klinger, R. (2020, December). Emotional People, Stimuli, or Targets: Which                  Semantic Roles Enable Machine Learning to Infer                  Emotions? Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and                  Emotions in Social Media.
    11. Armbrust, F., Schäfer, H., & Klinger, R. (2020). A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors. Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020). http://www.romanklinger.de/publications/ArmbrustSchaeferKlinger2020.pdf
    12. Troiano, E., Klinger, R., & Padó, S. (2020). Lost in Back-Translation: Emotion Preservation in Neural Machine Translation. Proceedings of the 28th International Conference on Computational Linguistics. http://www.romanklinger.de/publications/TroianoKlingerPado-coling2020.pdf
  5. 2019

    1. Bostan, L. A. M., & Klinger, R. (2019, June). Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis. Proceedings of the 10th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis.
    2. McHardy, R., Adel, H., & Klinger, R. (2019, June). Adversarial Training for Satire Detection: Controlling for Confounding Variables. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics. http://www.romanklinger.de/publications/McHardyAdelKlinger-NAACL2019.pdf
    3. Ehrlicher, H., Klinger, R., Lehmann, J., & Padó, S. (2019). Measuring Historical Emotions and Their Evolution: An Interdisciplinary Endeavour to Investigate The  `Emotions of Encounter’. Laboratório Interdisciplinar Sobre Informa\,Cão e Conhecimento Em Revista (Liinc Em Revista), 15(1), Article 1. https://doi.org/10.18617/liinc.v15i1.4557
    4. Kim, E., & Klinger, R. (2019). An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling. Proceedings of the Second Workshop of Storytelling.
    5. Kim, E., & Klinger, R. (2019, June). Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics. http://www.romanklinger.de/publications/KimKlingerNAACL2019.pdf
    6. Barnes, J., & Klinger, R. (2019). Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.11561
    7. Troiano, E., Padó, S., & Klinger, R. (2019). Crowdsourcing and Validating Event-focused Emotion Corpora for German and English. Proceedings of the Annual Conference of the Association for Computational Linguistics.
  6. 2018

    1. Strohm, F., & Klinger, R. (2018, October). An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs. The 5th IEEE International Conference on Data Science and Advanced Analytics, Special Track on Sentiment, Emotion, and Credibility of Information in Social Data. https://doi.org/10.1109/DSAA.2018.00087
    2. Kicherer, H., Dittrich, M., Grebe, L., Scheible, C., & Klinger, R. (2018). What You Use, Not What You Do: Automatic Classification and Similarity Detection of Recipes. Data and Knowledge Engineering. https://www.sciencedirect.com/science/article/pii/S0169023X17305402
    3. Barnes, J., Klinger, R., & Schulte im Walde, S. (2018, July). Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. http://www.romanklinger.de/publications/barnes-et-al-2018.pdf
    4. Bostan, L. A. M., & Klinger, R. (2018, August). A Survey on Annotated Data Sets for Emotion Classification in Text. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. http://www.romanklinger.de/publications/BostanKlinger2018coling.pdf
    5. Kim, J., & Klinger, R. (2018, August). Who Feels What and Why? An Annotated Corpus of Modern Literature of Semantic Roles in Emotions. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. http://www.romanklinger.de/publications/kimklinger2018reman.pdf
    6. Adel, H., Bostan, L. A. M., Papay, S., Padó, S., & Klinger., R. (2018). DERE: A task and domain-independent slot filling framework for declarative relation extraction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. http://aclweb.org/anthology/D18-2008
    7. Braun, M., Klinger, R., Padó, S., & Viehhauser, G. (2018, March). Digitale Modellierung von Figurenkomplexität am Beispiel des Parzival von Wolfram von Eschenbach. Book of Abstracts -- Digital Humanities Im Deutschsprachigen Raum. http://www.romanklinger.de/publications/BraunKlingerPadoViehhauser2018.html
    8. Hartung, M., ter Horst, H., Grimm, F., Diekmann, T., Klinger, R., & Cimiano, P. (2018). SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling. Proceedings of ACL 2018, System Demonstrations.
    9. Thorne, C., & Klinger, R. (2018). On the Semantic Similarity of Disease Mentions in MEDLINE and Twitter. Natural Language Processing and Information Systems: 23rd International Conference on Applications of                  Natural Language to Information Systems, NLDB 2018,                  Paris, France, June 13-15, 2018, Proceedings. https://link.springer.com/chapter/10.1007/978-3-319-91947-8_34
    10. Barth, F., Kim, E., Murr, S., & Klinger, R. (2018, March). A Reporting Tool for Relational Visualization and Analysis of Character Mentions in Literature. Book of Abstracts -- Digital Humanities Im Deutschsprachigen Raum. http://www.romanklinger.de/publications/BarthKimMurrKlinger2018.html
    11. Barnes, J., Klinger, R., & Schulte im Walde, S. (2018, August). Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. http://www.romanklinger.de/publications/BarnesKlingerSIW2018blsedomain.pdf
    12. Klinger, R., de Clercq, O., Mohammad, S. M., & Balahur, A. (2018, November). IEST: WASSA-2018 Implicit Emotions Shared Task. Proceedings of the 9th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis. http://aclweb.org/anthology/W18-6206
    13. Ter Horst, H., Hartung, M., Klinger, R., Brazda, N., Müller, H. W., & Cimiano, P. (2018). Assessing the Impact of Single and Pairwise Slot                  Constraints in a Factor Graph Model for                  Template-based Information Extraction. Natural Language Processing and Information Systems:                  23rd International Conference on Applications of                  Natural Language to Information Systems, NLDB 2018,                  Paris, France, June 13-15, 2018, Proceedings. https://link.springer.com/chapter/10.1007/978-3-319-91947-8_18
  7. 2017

    1. Sänger, M., Leser, U., & Klinger, R. (2017). Fine-grained Opinion Mining from Mobile App Reviews with Word Embedding Features. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 3--14). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-59569-6_1
    2. Thorne, C., & Klinger, R. (2017, September). Towards Confidence Estimation for Typed Protein-Protein Relation Extraction. Proceedings of the Biomedical NLP Workshop Associated with RANLP. http://www.romanklinger.de/publications/Thorne2017.pdf
    3. Brazda, N., ter Horst, H., Hartung, M., Wiljes, C., Estrada, V., Klinger, R., Kuchinke, W., Müller, H. W., & Cimiano, P. (2017, September). SCIO: An Ontology to Support the Formalization of Pre-Clinical Spinal Cord Injury Experiments. Workshop on Ontologies and Data in Life Sciences (ODLS 2017), Joint Workshops on Ontologies (JOWO). https://pub.uni-bielefeld.de/download/2913603/2913881
    4. Hartung, M., Klinger, R., Mohme, J., Vogel, L., & Schmidtke, F. (2017). Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. http://aclweb.org/anthology/W/W17/W17-5204.pdf
    5. ter Horst, H., Hartung, M., Klinger, R., Zwick, M., & Cimiano, P. (2017). Predicting Disease-Gene Associations using                  Cross-Document Graph-based Features. ArXiv E-Prints. https://arxiv.org/abs/1709.09239
    6. Reiter, N., Schulz, S., Kremer, G., Klinger, R., Viehhauser, G., & Kuhn, J. (2017). Teaching Computational Aspects in the Digital Humanities  Program at University of Stuttgart -- Intentions and  Experiences. Proceedings of the GSCL Workshop on Teaching NLP for Digital Humanities (Teach4DH 2017). http://ceur-ws.org/Vol-1918/reiter.pdf
    7. Hartung, M., Klinger, R., Schmidtke, F., & Vogel, L. (2017). Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 320--325). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-59569-6_40
    8. Kicherer, H., Dittrich, M., Grebe, L., Scheible, C., & Klinger, R. (2017). What You Use, Not What You Do: Automatic Classification of Recipes. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 197--209). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-59569-6_22
    9. Klinger, R. (2017). Does Optical Character Recognition and Caption Generation Improve Emotion Detection in Microblog Posts? In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017 (pp. 313--319). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-59569-6_39
    10. Barnes, J., Klinger, R., & Schulte im Walde, S. (2017). Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social                  Media Analysis. https://aclanthology.org/W17-5202/
    11. Kim, E., Padó, S., & Klinger, R. (2017, August). Prototypical Emotion Developments in Literary Genres. Digital Humanities 2017: Conference Abstracts. http://www.romanklinger.de/publications/kim2017.pdf
    12. Kim, E., Padó, S., & Klinger, R. (2017). Investigating the Relationship between Literary Genres and Emotional Plot Development. Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL). https://aclanthology.org/W17-2203/
    13. Köper, M., Kim, E., & Klinger, R. (2017). IMS at EmoInt-2017: Emotion Intensity Prediction                  with Affective Norms, Automatically Extended                  Resources and Deep Learning. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social                  Media Analysis. http://aclweb.org/anthology/W/W17/W17-5206.pdf
    14. Schuff, H., Barnes, J., Mohme, J., Padó, S., & Klinger, R. (2017). Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus. Proceedings of the 8th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis. http://aclweb.org/anthology/W/W17/W17-5203.pdf
  8. 2016

    1. Scheible, C., Klinger, R., & Padó, S. (2016). Model Architectures for Quotation Detection. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1736--1745. http://www.aclweb.org/anthology/P16-1164
    2. Klinger, R., Suliya, S. S., & Reiter, N. (2016). Automatic Emotion Detection for Quantitative Literary Studies -- A case study based on Franz Kafka’s ``Das Schloss’’ and ``Amerika’’. Digital Humanities 2016: Conference Abstracts, 826--828. http://dh2016.adho.org/abstracts/318
    3. Ling, J., & Klinger, R. (2016). An Empirical, Quantitative Analysis of the Differences Between Sarcasm and Irony. In H. Sack, G. Rizzo, N. Steinmetz, D. Mladenić, S. Auer, & C. Lange (Eds.), The Semantic Web: ESWC 2016 Satellite Events,                  Heraklion, Crete, Greece, May 29 -- June 2, 2016,                  Revised Selected Papers (pp. 203--216). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-47602-5_39
    4. Sänger, M., Leser, U., Kemmerer, S., Adolphs, P., & Klinger, R. (2016). SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German. In N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2016/summaries/59.html
    5. Jaskolski, J., Siegberg, F., Tibroni, T., Cimiano, P., & Klinger, R. (2016). Opinion Mining in Online Reviews About Distance Education Programs. http://arxiv.org/abs/1607.06299
  9. 2015

    1. Kessler, W., Klinger, R., & Kuhn, J. (2015). Towards Opinion Mining from Reviews for the Prediction of Product Rankings. Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 51--57. http://aclweb.org/anthology/W15-2908
    2. Stöckel, A., Paassen, B., Dickfelder, R., Göpfert, J. P., Brazda, N., Müller, H. W., Cimiano, P., Hartung, M., & Klinger, R. (2015). SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. BioRxiv. http://dx.doi.org/10.1101/013458
  10. 2014

    1. Hartung, M., Klinger, R., Zwick, M., & Cimiano, P. (2014). Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach. Proceedings of BioNLP 2014, 118–127. http://www.aclweb.org/anthology/W14-3418
    2. Bagewadi, S., Bobić, T., Hofmann-Apitius, M., Fluck, J., & Klinger, R. (2014). Detecting miRNA Mentions and Relations in Biomedical Literature version  3; referees: 2 approved, 1 approved with reservations. F1000Research, 3(205), Article 205. http://dx.doi.org/10.12688/f1000research.4591.3
    3. Klinger, R., & Cimiano, P. (2014). The USAGE review corpus for fine grained multi lingual opinion analysis. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) (pp. 2211–2218). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2014/pdf/85_Paper.pdf
    4. Ruppenhofer, J., Klinger, R., Struß, J. M., Sonntag, J., & Wiegand, M. (2014). IGGSA Shared Tasks on German Sentiment Analysis. In G. Faaß & J. Ruppenhofer (Eds.), Workshop Proceedings of the 12th Edition of the KONVENS Conference. University of Hildesheim. http://opus.bsz-bw.de/ubhi/volltexte/2014/319/pdf/04_01.pdf
    5. Buschmeier, K., Cimiano, P., & Klinger, R. (2014). An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 42–49. http://www.aclweb.org/anthology/W14-2608
    6. Paassen, B., Stöckel, A., Dickfelder, R., Göpfert, J. P., Brazda, N., Kirchhoffer, T., Müller, H. W., Klinger, R., Hartung, M., & Cimiano, P. (2014). Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. Proceedings of the Third Workshop on Semantic Web and Information Extraction, 25–32. http://www.aclweb.org/anthology/W14-6204
  11. 2013

    1. McCrae, J. P., Cimiano, P., & Klinger, R. (2013). Orthonormal Explicit Topic Analysis for Cross-Lingual Document Matching. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1732–1740. http://www.aclweb.org/anthology/D13-1179
    2. Klinger, R., & Cimiano, P. (2013). Joint and Pipeline Probabilistic Models for Fine-Grained Sentiment  Analysis: Extracting Aspects, Subjective Phrases and their Relations. 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), 937–944. http://dx.doi.org/10.1109/ICDMW.2013.13
    3. Klinger, R., & Cimiano, P. (2013). Bi-directional Inter-dependencies of Subjective Expressions and Targets and their Value for a Joint Model. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 848–854. http://www.aclweb.org/anthology/P13-2147
    4. Bobic, T., & Klinger, R. (2013). Committee-based Selection of Weakly Labeled Instances for Learning Relation Extraction. Research in Computing Science, 70, 187–197. http://www.micai.org/rcs/2013_70/Committee-based%20Selection%20of%20Weakly%20Labeled%20Instances%20for%20Learning%20Relation%20Extraction.html
  12. 2012

    1. Klinger, R., Senger, P., Madan, S., & Jacovi, M. (2012). Online Communities Support Policy-Making: The Need for Data Analysis. In E. Tambouris, A. Macintosh, & Ø. Sæbø (Eds.), Electronic Participation (Vol. 7444, pp. 132–143). Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-33250-0_12
    2. Aisopos, F., Kardara, M., Senger, P., Klinger, R., Papaoikonomou, A., Tserpes, K., Gardner, M., & Varvarigou, T. A. (2012). E-Government and Policy Simulation in Intelligent Virtual Environments. In K.-H. Krempels & J. Cordeiro (Eds.), WEBIST (pp. 129–135). SciTePress. http://dblp.uni-trier.de/db/conf/webist/webist2012.html#AisoposKSKPTGV12
    3. Bobic, T., Klinger, R., Thomas, P., & Hofmann-Apitius, M. (2012). Improving Distantly Supervised Extraction of Drug-Drug and Protein-Protein Interactions. Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, 35–43. http://www.aclweb.org/anthology/W12-0705
    4. Thomas, P., Bobić, T., Leser, U., Hofmann-Apitius, M., & Klinger, R. (2012). Weakly Labeled Corpora as Silver Standard for Drug-Drug and Protein-Protein Interaction. Proceedings of the Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM) on Language Resources and Evaluation Conference (LREC). http://www.romanklinger.de/publications/ppi-ddi.pdf
  13. 2011

    1. Klinger, R. (2011). Conditional Random Fields for Named Entity Recognition - Feature Selection and Optimization in Biology and Chemistry. In Fraunhofer Series in Information and Communication Technology. Shaker. http://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-0213-3
    2. Klinger, R., Riedel, S., & McCallum, A. (2011). Inter-Event Dependencies support Event Extraction from Biomedical Literature. Mining Complex Entities from Network and Biomedical Data (MIND), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). http://www.romanklinger.de/publications/klinger11interevent.pdf
    3. Klinger, R. (2011). Automatically Selected Skip Edges in Conditional Random Fields for Named Entity Recognition. Proceedings of the International Conference Recent Advances in Natural Language Processing 2011, 580–585. http://aclweb.org/anthology/R11-1082
    4. Thomas, P., Solt, I., Klinger, R., & Leser, U. (2011). Learning Protein Protein Interaction Extraction using Distant Supervision. Proceedings of Workshop on Robust Unsupervised and Semisupervised Methods in Natural Language Processing, 25–32. http://www.aclweb.org/anthology/W11-3904
    5. Thomas, P. E., Klinger, R., Furlong, L. I., Hofmann-Apitius, M., & Friedrich, C. M. (2011). Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers. BMC Bioinformatics, 12(Suppl 4)(S4), Article S4. http://dx.doi.org/10.1186/1471-2105-12-S4-S4
  14. 2010

    1. Müller, B., Klinger, R., Gurulingappa, H., Mevissen, H.-T., Hofmann-Apitius, M., Fluck, J., & Friedrich, C. M. (2010). Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities. Proceedings of the 1st IRF Conference. http://link.springer.com/chapter/10.1007/978-3-642-13084-7_12
    2. Gurulingappa, H., Klinger, R., Hofmann-Apitius, M., & Fluck, J. (2010, May). An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature. 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining (7th Edition of the Language Resources and Evaluation Conference). http://www.nactem.ac.uk/biotxtm/papers/Gurulingappa.pdf
  15. 2009

    1. Klinger, R., & Friedrich, C. M. (2009). Feature Subset Selection in Conditional Random Fields for Named Entity Recognition. In G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov, & N. Nikolov (Eds.), Proceedings of Recent Advances in Natural Language Processing (RANLP) (pp. 185–191). http://aclweb.org/anthology-new/R/R09/R09-1035.pdf
    2. Risselada, R., Friedrich, C. M., Ebeling, C., Klinger, R., Bauer-Mehren, A., Pastor, M., Villa, M. C., Pozo, J. M., Frangi, A. F., & Hofmann-Apitius, M. (2009). Workflows for Data Mining in Integrated multi-modal Data of Intracranial Aneurysms using KNIME. Book of Abstracts of the R User Conference (UseR!), 165. http://math.agrocampus-ouest.fr/infoglueDeliverLive/digitalAssets/20008_Risselada_Friedrich_Ebeling_Klinger.pdf
    3. Gurulingappa, H., Müller, B., Klinger, R., Mevissen, H.-T., Hofmann-Apitius, M., Fluck, J., & Friedrich, C. M. (2009). Patent Retrieval in Chemistry based on semantically tagged Named Entities. In E. M. Voorhees & L. P. Buckland (Eds.), The Eighteenth Text RETrieval Conference (TREC 2009) Proceedings. http://trec.nist.gov/pubs/trec18/papers/scai.CHEM.pdf
    4. Kolářik, C., Klinger, R., & Hofmann-Apitius, M. (2009). Identification of Histone Modifications in Biomedical Text for Supporting Epigenomic Research. BMC Bioinformatics, 10(S28), Article S28. http://dx.doi.org/10.1186/1471-2105-10-S1-S28
  16. 2008

    1. Kolarik, C., Klinger, R., Friedrich, C. M., Hofmann-Apitius, M., & Fluck, J. (2008). Chemical Names: Terminological Resources and Corpora Annotation. Workshop on Building and Evaluating Resources for Biomedical Text Mining (6th Edition of the Language Resources and Evaluation Conference), 51–58.
    2. Klinger, R., Kolářik, C., Fluck, J., Hofmann-Apitius, M., & Friedrich, C. M. (2008). Detection of IUPAC and IUPAC-like Chemical Names. Bioinformatics, 24(13), Article 13. http://dx.doi.org/10.1093/bioinformatics/btn181
    3. Smith, L., Tanabe, L. K., nee Ando, R. J., Juo, C.-J., Chung, I.-F., Hsu, C.-N., Lin, Y.-S., Klinger, R., Friedrich, C. M., Ganchev, K., Torii, M., Liu, H., Haddow, B., Struble, C. A., Povinelli, R. J., Vlachos, A., Baumgartner Jr., W. A., Hunter, L., Carpenter, B., … Wilbur, W. J. (2008). Overview of BioCreative II Gene Mention Recognition. Genome Biology, 9(Suppl 2), Article Suppl 2. https://doi.org/10.1186/gb-2008-9-s2-s2
    4. Hofmann-Apitius, M., Fluck, J., Furlong, L., Fornes, O., Kolárik, C., Hanser, S., Boecker, M., Schultz, S., Sanz, F., Klinger, R., Mevissen, T., Gatterneyer, T., Oliva, B., & Friedrich, C. (2008). Knowledge Environments Representing Molecular Entities for the Virtual Physiological Human. Philosophical Transactions of the Royal Society A. http://dx.doi.org/10.1098/rsta.2008.0099
  17. 2007

    1. Klinger, R., Friedrich, C. M., Fluck, J., & Hofmann-Apitius, M. (2007). Named Entity Recognition with Combinations of Conditional Random Fields. Proceedings of the Second BioCreative Challenge Evaluation Workshop, 89–91.
    2. Klinger, R., & Tomanek, K. (2007). Classical Probabilistic Models and Conditional Random Fields (TR07-2–013; Issues TR07-2–013). Department of Computer Science, Dortmund University of Technology. https://ls11-www.cs.uni-dortmund.de/_media/techreports/tr07-13.pdf
    3. Klinger, R., Furlong, L. I., Friedrich, C. M., Mevissen, H. T., Fluck, J., Sanz, F., & Hofmann-Apitius, M. (2007). Identifying Gene Specific Variations in Biomedical Text. Journal of Bioinformatics and Computational Biology, 5(6), Article 6. http://dx.doi.org/10.1142/S0219720007003156
  18. 2005

    1. Bützken, M., Edelkamp, S., Elalaoui, A., Kahl, K., Karmouni, R., Klinger, R., Lahiane, K., Matuszewski, A., Mehler, T., Nazih, M., Nelskamp, M., & Wiggers, A. (2005). An Integrated Toolkit for Modern Action Planning. 19th Workshop on New Results in Planning, Scheduling and Design (PUK), 1–11. http://www.puk-workshop.de/puk2005/paper/1_puk1.pdf
  • Information Extraction and Retrieval
  • Sentiment and Opinion Mining
  • Natural Language Processing
  • Biomedical NLP, Bioinformatics
  • Supervised and semi-supervised machine learning methods
  • Graphical Models
  • Data Mining/Text Mining
  • Evolutionary Computation
  • Computational Intelligence/Artificial Intelligence
  • Automatic Composition of Music

For help with finding a topic, please read this website.

  • If you are interested in writing your thesis under my supervision in one of the following topics, please contact me:
    • Information Extraction
    • Sentiment Analysis
    • Bio-medical natural language processing
    • Web-Mining
    • Ontologies
    • Machine Learning
    • Statistical Methods
  • Examples for supervised theses in the past:
    • A machine learning approach to sentiment analysis for distance education evaluation 
    • Ranked Protein-Disease Relations Using Machine-Learning
    • Entwicklung eines Systems zur Erkennung von Ironie in Text
    • Joint Extraction of Proteins and Bio-Molecular Events using Imperatively Defined Factor Graphs
    • Auswirkung des aktiven Lernens auf die Merkmalsselektion bei Textklassikation
    • Twitter als Wahlindikator
    • Stimmungs- und Themenerkennung in politischen Debatten
    • Automated extraction of variation mentions from literature sources and mapping to a unique database identifier

Website with more information: https://www.romanklinger.de/

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