This image shows Dominik Schlechtweg

Dominik Schlechtweg

Dr.

Junior research group leader
Institute for Natural Language Processing (IMS)
Foundations of Computational Linguistics

Contact

+49 711 685 81361

Pfaffenwaldring 5 b
70569 Stuttgart
Deutschland
Room: 01.005

Office Hours

Upon email request.

Subject

I did my PhD at the IMS (University of Stuttgart) working together with Sabine Schulte im Walde on automatic detection of lexical semantic change. I held a PhD scholarship from Konrad Adenauer Foundation. After my PhD I did a research internship with Katrin Erk at the University of Texas, Austin. Since February 2022 I am an independent research group leader at the IMS (University of Stuttgart) working in the 6-year research program Change is Key!.

Find more information on my website.

  1. D. Schlechtweg, T. Choppa, W. Zhao, and M. Roth, “CoMeDi Shared Task: Median Judgment Classification & Mean Disagreement Ranking with Ordinal Word-in-Context Judgments,” in Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation, M. Roth and D. Schlechtweg, Eds., Abu Dhabi, UAE: International Committee on Computational Linguistics, Jan. 2025, pp. 33–47. [Online]. Available: https://aclanthology.org/2025.comedi-1.4/
  2. Y. X. Loke, D. Schlechtweg, and W. Zhao, “ABDN-NLP at CoMeDi Shared Task: Predicting the Aggregated Human Judgment via Weighted Few-Shot Prompting,” in Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation, M. Roth and D. Schlechtweg, Eds., Abu Dhabi, UAE: International Committee on Computational Linguistics, Jan. 2025, pp. 122–128. [Online]. Available: https://aclanthology.org/2025.comedi-1.12/
  3. T. Choppa, M. Roth, and D. Schlechtweg, “Predicting Median, Disagreement and Noise Label in Ordinal Word-in-Context Data,” in Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation, M. Roth and D. Schlechtweg, Eds., Abu Dhabi, UAE: International Committee on Computational Linguistics, Jan. 2025, pp. 65–77. [Online]. Available: https://aclanthology.org/2025.comedi-1.6/
  4. D. Schlechtweg, S. M. Virk, and N. Arefyev, “The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks.” 2024.
  5. E. Sköldberg, S. M. Virk, P. Sander, S. Hengchen, and D. Schlechtweg, “Revealing semantic variation in Swedish using computational models of semantic proximity: results from lexicographical experiments,” in 21st EURALEX International Congress Lexicography and Semantics, 2024. [Online]. Available: https://euralex.jezik.hr/wp-content/uploads/2021/09/Euralex-XXI-proceedings_1st.pdf
  6. X. Ma, D. Schlechtweg, and W. Zhao, “Presence or Absence: Are Unknown Word Usages in Dictionaries?,” in Proceedings of the 5th Workshop on Computational Approaches to Historical Language Change, N. Tahmasebi, S. Montariol, A. Kutuzov, D. Alfter, F. Periti, P. Cassotti, and N. Huebscher, Eds., Bangkok, Thailand: Association for Computational Linguistics, Aug. 2024, pp. 42–54. [Online]. Available: https://aclanthology.org/2024.lchange-1.5
  7. D. Schlechtweg, P. Cassotti, B. Noble, D. Alfter, S. S. im Walde, and N. Tahmasebi, “More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida: Association for Computational Linguistics, Nov. 2024.
  8. A. Kutuzov, M. Fedorova, D. Schlechtweg, and N. Arefyev, “Enriching Word Usage Graphs with Cluster Definitions,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, and N. Xue, Eds., Torino, Italia: ELRA and ICCL, May 2024, pp. 6189–6198. [Online]. Available: https://aclanthology.org/2024.lrec-main.546
  9. F. Periti, P. Cassotti, S. Montanelli, N. Tahmasebi, and D. Schlechtweg, “TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida: Association for Computational Linguistics, Nov. 2024.
  10. D. Schlechtweg, F. D. Zamora-Reina, F. Bravo-Marquez, and N. Arefyev, “Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection,” Language Resources and Evaluation, 2024, [Online]. Available: https://doi.org/10.1007/s10579-024-09771-7
  11. J. Lautenschlager, E. Sköldberg, S. Hengchen, and D. Schlechtweg, “Detection of Non-recorded Word Senses in English and Swedish.” 2024.
  12. S. Yadav, T. Choppa, and D. Schlechtweg, “Towards Automating Text Annotation: A Case Study on Semantic Proximity Annotation using GPT-4.” 2024. [Online]. Available: https://arxiv.org/abs/2407.04130
  13. P. Sander et al., “The DURel Annotation Tool: Using fine-tuned LLMs to discover non-recorded senses in multiple languages,” in Workshop on Large Language Models and Lexicography at 21st EURALEX International Congress Lexicography and Semantics, 2024. [Online]. Available: https://www.cjvt.si/wp-content/uploads/2024/10/LLM-Lex_2024_Book-of-Abstracts.pdf
  14. D. Schlechtweg et al., “The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, N. Aletras and O. De Clercq, Eds., St. Julians, Malta: Association for Computational Linguistics, Mar. 2024, pp. 137–149. [Online]. Available: https://aclanthology.org/2024.eacl-demo.15
  15. J. Chen, E. Chersoni, D. Schlechtweg, J. Prokic, and C.-R. Huang, “ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection,” in Proceedings of the 4th International Workshop on Computational Approaches to Historical Language Change, Singapore: Association for Computational Linguistics, 2023.
  16. D. Schlechtweg, “Human and computational measurement of lexical semantic change,” Dissertation, Universität Stuttgart, Stuttgart, 2023. doi: 10.18419/opus-12833.
  17. F. D. Zamora-Reina, F. Bravo-Marquez, and D. Schlechtweg, “LSCDiscovery: A shared task on semantic change discovery and detection in Spanish,” in Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 149–164. doi: 10.18653/v1/2022.lchange-1.16.
  18. G. Baldissin, D. Schlechtweg, and S. Schulte im Walde, “DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France: European Language Resources Association, Jun. 2022, pp. 2601–2609. [Online]. Available: https://aclanthology.org/2022.lrec-1.278
  19. D. Frassinelli, G. Lapesa, R. Alatrash, D. Schlechtweg, and S. Schulte im Walde, “Regression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch,” in Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects, Kiyv, Ukraine: Association for Computational Linguistics, Apr. 2021, pp. 21–27. [Online]. Available: https://aclanthology.org/2021.vardial-1.3
  20. J. Kaiser, S. Kurtyigit, S. Kotchourko, and D. Schlechtweg, “Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online: Association for Computational Linguistics, Apr. 2021, pp. 125–137. doi: 10.18653/v1/2021.eacl-main.10.
  21. T. Bott, D. Schlechtweg, and S. Schulte im Walde, “More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online: Association for Computational Linguistics, Aug. 2021, pp. 186–192. doi: 10.18653/v1/2021.findings-acl.16.
  22. D. Schlechtweg, N. Tahmasebi, S. Hengchen, H. Dubossarsky, and B. McGillivray, “DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 7079–7091. doi: 10.18653/v1/2021.emnlp-main.567.
  23. S. Laicher, S. Kurtyigit, D. Schlechtweg, J. Kuhn, and S. Schulte im Walde, “Explaining and Improving BERT Performance on Lexical Semantic Change Detection,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, Online: Association for Computational Linguistics, Apr. 2021, pp. 192–202. doi: 10.18653/v1/2021.eacl-srw.25.
  24. S. Hengchen, N. Tahmasebi, D. Schlechtweg, and H. Dubossarsky, “Challenges for Computational Lexical Semantic Change,” in Computational Approaches to Semantic Change, vol. Language Variation, N. Tahmasebi, L. Borin, A. Jatowt, Y. Xu, and S. Hengchen, Eds., Berlin: Language Science Press, 2021. [Online]. Available: https://arxiv.org/abs/2101.07668v1
  25. D. Schlechtweg, E. Castaneda, J. Kuhn, and S. Schulte im Walde, “Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model,” in Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, Online: Association for Computational Linguistics, Aug. 2021, pp. 241–251. doi: 10.18653/v1/2021.starsem-1.23.
  26. S. Kurtyigit, M. Park, D. Schlechtweg, J. Kuhn, and S. Schulte im Walde, “Lexical Semantic Change Discovery,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online: Association for Computational Linguistics, Aug. 2021, pp. 6985–6998. doi: 10.18653/v1/2021.acl-long.543.
  27. J. Kaiser, D. Schlechtweg, and S. Schulte im Walde, “OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection,” in Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), V. Basile, D. Croce, M. Di Maro, and L. C. Passaro, Eds., Online: CEUR.org, 2020. [Online]. Available: https://arxiv.org/abs/2011.03258
  28. D. Schlechtweg, B. McGillivray, S. Hengchen, H. Dubossarsky, and N. Tahmasebi, “SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection,” in Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain: Association for Computational Linguistics, 2020. [Online]. Available: https://arxiv.org/abs/2007.11464
  29. A. Ahmad, K. Desta, F. Lang, and D. Schlechtweg, “Shared Task: Lexical Semantic Change Detection in German,” CoRR, vol. abs/2001.07786, 2020, [Online]. Available: https://arxiv.org/abs/2001.07786
  30. D. Schlechtweg and S. Schulte im Walde, “Simulating Lexical Semantic Change from Sense-Annotated Data,” in The Evolution of Language: Proceedings of the 13th International Conference (EvoLang13), A. Ravignani, C. Barbieri, M. Martins, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, K. Mudd, and T. Verhoef, Eds., 2020. doi: 10.17617/2.3190925.
  31. A. Hätty, D. Schlechtweg, M. Dorna, and S. Schulte im Walde, “Predicting Degrees of Technicality in Automatic Terminology Extraction,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online: Association for Computational Linguistics, Jul. 2020, pp. 2883–2889. doi: 10.18653/v1/2020.acl-main.258.
  32. S. Laicher, G. Baldissin, E. Castaneda, D. Schlechtweg, and S. Schulte im Walde, “CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection,” in Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), V. Basile, D. Croce, M. Di Maro, and L. C. Passaro, Eds., Online: CEUR.org, 2020.
  33. J. Kaiser, D. Schlechtweg, S. Papay, and S. Schulte im Walde, “IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection,” in Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain: Association for Computational Linguistics, 2020. [Online]. Available: https://arxiv.org/abs/2008.03164
  34. R. Alatrash, D. Schlechtweg, J. Kuhn, and S. Schulte im Walde, “CCOHA: Clean Corpus of Historical American English,” in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille, France: European Language Resources Association, May 2020, pp. 6958–6966. [Online]. Available: https://www.aclweb.org/anthology/2020.lrec-1.859
  35. H. Dubossarsky, S. Hengchen, N. Tahmasebi, and D. Schlechtweg, “Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change.,” in ACL (1), A. Korhonen, D. R. Traum, and L. Màrquez, Eds., Association for Computational Linguistics, 2019, pp. 457–470. [Online]. Available: https://www.aclweb.org/anthology/P19-1044/
  36. A. Hätty, D. Schlechtweg, and S. Schulte im Walde, “SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction.,” in *SEM@NAACL-HLT, R. Mihalcea, E. Shutova, L.-W. Ku, K. Evang, and S. Poria, Eds., Association for Computational Linguistics, 2019, pp. 1–8. [Online]. Available: https://www.aclweb.org/anthology/S19-1001/
  37. D. Schlechtweg, C. Oguz, and S. Schulte im Walde, “Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling,” in Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Florence, Italy: Association for Computational Linguistics, Aug. 2019, pp. 24–30. doi: 10.18653/v1/W19-4803.
  38. D. Schlechtweg, A. Hätty, M. Del Tredici, and S. Schulte im Walde, “A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains.,” in ACL (1), A. Korhonen, D. R. Traum, and L. Màrquez, Eds., Association for Computational Linguistics, 2019, pp. 732–746. [Online]. Available: https://www.aclweb.org/anthology/P19-1072/
  39. D. Schlechtweg and S. Schulte im Walde, “Distribution-based prediction of the degree of grammaticalization for German prepositions,” in The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII), C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, and T. Verhoef, Eds., Online at http://evolang.org/torun/proceedings/papertemplate.html?p=169, 2018. [Online]. Available: https://evolang.org/torun/proceedings/papertemplate.html?p=169
  40. D. Schlechtweg, S. Schulte im Walde, and S. Eckmann, “Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change.,” in NAACL-HLT (2), M. A. Walker, H. Ji, and A. Stent, Eds., Association for Computational Linguistics, 2018, pp. 169–174. [Online]. Available: https://www.aclweb.org/anthology/N18-2027/
  41. D. Schlechtweg, S. Eckmann, E. Santus, S. Schulte im Walde, and D. Hole, “German in Flux: Detecting Metaphoric Change via Word Entropy.,” in CoNLL, R. Levy and L. Specia, Eds., Association for Computational Linguistics, 2017, pp. 354–367. [Online]. Available: https://www.aclweb.org/anthology/K17-1036/
  42. V. Shwartz, E. Santus, and D. Schlechtweg, “Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection.,” in EACL (1), M. Lapata, P. Blunsom, and A. Koller, Eds., Association for Computational Linguistics, 2017, pp. 65–75. [Online]. Available: https://www.aclweb.org/anthology/E17-1007/
  43. D. Schlechtweg, “Exploitation of Co-reference in Distributional Semantics.,” in LREC, N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, and S. Piperidis, Eds., European Language Resources Association (ELRA), 2016. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2016/summaries/195.html
since
02/2022		 Change is Key! (CiK).

 

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