Steffen Remus

Publications

  1. Fabian Barteld, Steffen Remus, Saba Anwar, Julian Stawecki, Alexander Ziem, and Chris Biemann. 2026. Joint Identification and Induction of Semantic Frames with Scalable Semi-Supervised Graph Clustering. In: Proceedings of the Fifteenth International Conference on Language Resources and Evaluation (LREC 2026). Palma, Mallorca, Spain, (10020–10030). (pdf+bib, git).

  2. Nikolas Bertrand, Akshay Devkate, Guido Juckeland, Jan Linxweiler, Sören Peters, Steffen Remus, Katrin Schöning-Stierand, and Anna-Lena Lamprecht. 2025. Compared Experiences from Teaching Full-Semester Research Software Engineering Courses at Four (German) Universities. In: Electronic Communications of the EASST 3, pp. 1–11. (link).

  3. Robert Günzler, Özge Sevgili, Steffen Remus, Chris Biemann, and Irina Nikishina. 2024. Sövereign at The Perspective Argument Retrieval Shared Task 2024: Using LLMs with Argument Mining. In: Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024). Bangkok, Thailand, pp. 150–158. (pdf).

  4. Lennart Roth, Katrin Schöning-Stierand, and Steffen Remus. 2024. A Comparison of Selective State Space Models and Transformers for Single-Step Retrosynthetic Reaction Prediction. In: Proceedings of 18th German Conference on Cheminformatics (GCC). Bad Soden am Taunus, Germany.

  5. Robert Geislinger, Ali Ebrahimi Pourasad, Deniz Gül, Daniel Djahangir, Seid Muhie Yimam, Steffen Remus, and Chris Biemann. 2023. Multi-Modal Learning Application – Support Language Learners with NLP Techniques and Eye-Tracking. In: Proceedings of the 1st Workshop on Linguistic Insights from and for Multimodal Language Processing (LIMO). Ingolstadt, Germany, pp. 6–11. (pdf, bib).

  6. Steffen Remus. 2023. Domain Defining Context: On Domain-Dependent Corpus Expansion and Contextualized Semantic Structuring. Doctoral dissertation. Hamburg, Germany: Universität Hamburg. (pdf, metadata).

  7. Özge Sevgili, Steffen Remus, Abhik Jana, Alexander Panchenko, and Chris Biemann. 2023. Unsupervised Ultra-Fine Entity Typing with Distributionally Induced Word Senses. In: Proceedings of the 11th International Conference on Analysis of Images, Social Networks and Texts (AIST). Yerevan, Armenia, pp. 1–15. (pdf).

  8. Tim Fischer, Steffen Remus, and Chris Biemann. 2022. Measuring Faithfulness of Abstractive Summaries. In: Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022). Potsdam, Germany, pp. 63–73. (pdf).

  9. Markus J Hofmann, Steffen Remus, Chris Biemann, and Ralph Radach. 2022. Language models explain word reading times better than empirical predictability. In: Frontiers in Artificial Intelligence. Ed. by Massimo Stella, pp. 1–20.

  10. Steffen Remus, Gregor Wiedemann, Saba Anwar, Fynn Petersen-Frey, Seid Muhie Yimam, and Chris Biemann. 2022. More Like This: Semantic Retrieval with Linguistic Information. In: Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022). Potsdam, Germany, pp. 156–166. (pdf).

  11. Gopalakrishnan Venkatesh, Abhik Jana, Steffen Remus, Özge Sevgili, Gopalakrishnan Srinivasaraghavan, and Chris Biemann. 2022. Using Distributional Thesaurus To Enhance Transformer-based Contextualized Representations for Low Resource Languages. In: Proceedings of the 37th ACM/SIGAPP Symposium On Applied Computing (ACM SAC), Special Track on Knowledge and Natural Language Processing (KNLP). online, pp. 845–852.

  12. Benjamin Milde, Tim Fischer, Steffen Remus, and Chris Biemann. 2021. MoM: Minutes of Meeting Bot. In: Proceedings of Interspeech 2021 Show&Tell. Brno, Czech Republic, pp. 3311–3312. (pdf, video-de, video-en, git).

  13. Jingyuan Feng, Özge Sevgili, Steffen Remus, Eugen Ruppert, and Chris Biemann. 2020. Supervised Pun Detection and Location with Feature Engineering and Logistic Regression. In: Proceedings of the 5th SwissText & 16th KONVENS Joint Conference 2020. Zurich, Switzerland, 3:1–6. (pdf).

  14. Markus J Hofmann, Steffen Remus, Chris Biemann, and Ralph Radach. 2020. Language models explain word reading times better than empirical predictability. In: PsyArXiv, pp. 1–77. (link).

  15. Dirk Johannßen, Chris Biemann, Steffen Remus, Timo Baumann, and David Scheffer. 2020. GermEval 2020 Task 1 on the Classification and Regression of Cognitive and Motivational style from Text. In: Proceedings of the GermEval 2020 Task 1 Workshop in conjunction with the 5th SwissText & 16th KONVENS Joint Conference 2020. Zurich, Switzerland (online), pp. 1–10. (pdf, web).

  16. Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, and Alexander Panchenko. 2020. Word sense disambiguation for 158 languages using word embeddings only. In: Proceedings of The 12th Language Resources and Evaluation Conference. Marseille, France, pp. 5943–5952. (pdf, bib, web).

  17. Rami Aly, Steffen Remus, and Chris Biemann. 2019. Hierarchical Multi-label Classification of Text with Capsule Networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Florence, Italy, pp. 323–330. (pdf, bib).

  18. Tim Fischer, Steffen Remus, and Chris Biemann. 2019. LT Expertfinder: An Evaluation Framework for Expert Finding Methods. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). Minneapolis, MN, USA, pp. 98–104. (pdf, bib, git, web).

  19. Markus J Hofmann, Steffen Remus, Chris Biemann, and Ralph Radach. 2019. Language models can outperform empirical predictability in predicting eye movement data. In: Proceedings of the 20th European Conference on Eye Movements (ECEM) 2019. Alicante, Spain. (poster-pdf).

  20. Steffen Remus, Rami Aly, and Chris Biemann. 2019. GermEval 2019 Task 1: Hierarchical Classification of Blurbs. In: Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019). Erlangen, Germany, pp. 280–292. (pdf, bib, web).

  21. Steffen Remus, Hanna Hedeland, Anne Ferger, Kristin Bührig, and Chris Biemann. 2019a. Annotation gesprochener Daten mit WebAnno-MM. In: Die 6. Jahrestagung des DHd e.V. 2019. Frankfurt & Mainz, Germany. (poster-pdf).

  22. Steffen Remus, Hanna Hedeland, Anne Ferger, Kristin Bührig, and Chris Biemann. 2019b. WebAnno-MM: EXMARaLDA meets WebAnno. In: Selected papers from the CLARIN Annual Conference 2018. Linköping Electronic Conference Proceedings 159, pp. 166–172. (pdf, git).

  23. Gregor Wiedemann, Steffen Remus, Avi Chawla, and Chris Biemann. 2019. Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings. In: Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019). Erlangen, Germany, pp. 161–170. (pdf, bib).

  24. Steffen Remus and Chris Biemann. 2018. Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan, pp. 1035–1041. (bib, pdf, poster, git).

  25. Steffen Remus, Hanna Hedeland, Anne Ferger, Kristin Bührig, and Chris Biemann. 2018. EXMARaLDA meets WebAnno. In: Proceedings of the CLARIN Annual Conference 2018 (CAC 2018). Pisa, Italy, pp. 1–5. (pdf).

  26. Seid Muhie Yimam, Steffen Remus, Alexander Panchenko, Andreas Holzinger, and Chris Biemann. 2017. Entity-Centric Information Access with the Human-in-the-Loop for the Biomedical Domains. In: Proceddings of the Biomedical NLP Workshop associated with RANLP 2017. Varna, Bulgaria, pp. 42–48. (pdf).

  27. Steffen Remus, Manuel Kaufmann, Kathrin Ballweg, Tatiana von Landesberger, and Chris Biemann. 2017. Storyfinder: Personalized Knowledge Base Construction and Management by Browsing the Web. In: CIKM ’17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore, Singapore, pp. 2519–2522. (pdf, poster, web/git).

  28. Markus J Hofmann, Chris Biemann, and Steffen Remus. 2016. Benchmarking n-grams, topic models and recurrent neural networks by cloze completions, EEGs and eye movements. In: Cognitive Approach to Natural Language Processing. Ed. by Bernadette Sharp, Florence Sèdes, and Wiesław Lubaszewski, pp. 197–215. (link).

  29. Alexander Panchenko, Stefano Faralli, Eugen Ruppert, Steffen Remus, Hubert Naets, Cedrick Fairon, Simone P Ponzetto, and Chris Biemann. 2016. TAXI at SemEval-2016 Task 13: A Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling. In: Proceedings of the 10th International Workshop on Semantic Evaluation. San Diego, CA, USA, pp. 1320–1327. (pdf).

  30. Steffen Remus and Chris Biemann. 2016. Domain-Specific Corpus Expansion with Focused Webcrawling. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). Portorož, Slovenia, pp. 23–28. (pdf, bib, web/git).

  31. Steffen Remus, Gerold Hintz, Darina Benikova, Thomas Arnold, Judith Eckle-Kohler, Christian M Meyer, Margot Mieskes, and Chris Biemann. 2016. EmpiriST: AIPHES Robust Tokenization and POS-Tagging for Different Genres. In: Proceedings of the 10th Web as Corpus Workshop (WAC-X). Berlin, Germany, pp. 106–114. (pdf).

  32. Chris Biemann, Steffen Remus, and Markus J Hofmann. 2015. Predicting word ’predictability’ in cloze completion, electroencephalographic and eye movement data. In: Proceedings of the 12th International Workshop on Natural Language Processing and Cognitive Science. Krakow, Poland, pp. 83–93. (pdf).

  33. Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. 2015. Do Supervised Distributional Methods Really Learn Lexical Inference Relations? In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver, CO, USA, pp. 970–976. (pdf, bib).

  34. Dirk Goldhahn, Steffen Remus, Uwe Quasthoff, and Chris Biemann. 2014. Top-Level Domain Crawling for Producing Comprehensive Monolingual Corpora from the Web. In: Proceedings of the LREC-14 workshop on Challenges in the Management of Large Corpora (CMLC-2). Reykjavik, Iceland, pp. 10–14. (pdf).

  35. Jinseok Nam, Christian Kirschner, Zheng Ma, Nicolai Erbs, Susanne Neumann, Daniela Oelke, Steffen Remus, Chris Biemann, Judith Eckle-Kohler, Johannes Fürnkranz, Iryna Gurevych, Marc Rittberger, and Karsten Weihe. 2014. Knowledge Discovery in Scientific Literature. In: Proceedings of the 12th Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2014). Hildesheim, Germany, pp. 66–76. (pdf).

  36. Steffen Remus. 2014. Unsupervised Relation Extraction of In-Domain Data from Focused Crawls. In: Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics. Gothenburg, Sweden, pp. 11–20. (pdf, bib).

  37. Steffen Remus and Chris Biemann. 2013. Three Knowledge-Free Methods for Automatic Lexical Chain Extraction. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Atlanta, GA, USA, pp. 989–999. (pdf, bib).

  38. Steffen Remus. 2012. Automatically Identifying Lexical Chains by Means of Statistical Methods – A Knowledge-Free Approach. MA. Darmstadt, Germany: Technische Universität Darmstadt.