This image shows Dominik Schlechtweg

Dominik Schlechtweg

Former employee
Institute for Natural Language Processing (IMS)

Contact

Pfaffenwaldring 5 b
Stuttgart
Deutschland

Office Hours

Upon email request.

Subject

I'm a PhD student working together with Sabine Schulte im Walde on automatic detection of lexical semantic change.

Find more information on my website.

  1. 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
  2. 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, 2021, pp. 125--137. doi: 10.18653/v1/2021.eacl-main.10.
  3. T. Bott, D. Schlechtweg, and S. Schulte im Walde, “More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods,” Online, 2021.
  4. 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, 2021, pp. 192--202. doi: 10.18653/v1/2021.eacl-srw.25.
  5. 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, 2021, pp. 21--27. [Online]. Available: https://www.aclweb.org/anthology/2021.vardial-1.3
  6. 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, 2021, pp. 7079--7091. [Online]. Available: https://aclanthology.org/2021.emnlp-main.567
  7. S. Kurtyigit, M. Park, D. Schlechtweg, J. Kuhn, and S. Schulte im Walde, “Lexical Semantic Change Discovery,” Online, 2021.
  8. 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, 2021, pp. 241--251. doi: 10.18653/v1/2021.starsem-1.23.
  9. A. Hätty, D. Schlechtweg, M. Dorna, and S. Schulte im Walde, “Predicting Degrees of Technicality in Automatic Terminology Extraction,” Seattle, Washington, 2020. [Online]. Available: https://www.aclweb.org/anthology/2020.acl-main.258/
  10. A. Ahmad, K. Desta, F. Lang, and D. Schlechtweg, “Shared Task: Lexical Semantic Change Detection in German,” CoRR, vol. abs/2001.07786, 2020.
  11. 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,” Barcelona, Spain, 2020. [Online]. Available: https://arxiv.org/abs/2008.03164
  12. 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, 2020, pp. 6958--6966. [Online]. Available: https://www.aclweb.org/anthology/2020.lrec-1.859
  13. 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), 2020. doi: 10.17617/2.3190925.
  14. D. Schlechtweg, B. McGillivray, S. Hengchen, H. Dubossarsky, and N. Tahmasebi, “SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection,” Barcelona, Spain, 2020. [Online]. Available: https://www.aclweb.org/anthology/2020.semeval-1.1/
  15. 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), Online, 2020. [Online]. Available: https://arxiv.org/abs/2011.07247
  16. 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), Online, 2020. [Online]. Available: https://arxiv.org/abs/2011.03258
  17. H. Dubossarsky, S. Hengchen, N. Tahmasebi, and D. Schlechtweg, “Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 457--470. [Online]. Available: https://www.aclweb.org/anthology/P19-1044/
  18. 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 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 732--746. [Online]. Available: https://www.aclweb.org/anthology/P19-1072/
  19. 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, 2019, pp. 24--30. [Online]. Available: https://www.aclweb.org/anthology/W19-4803/
  20. A. Hätty, D. Schlechtweg, and S. Schulte im Walde, “SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction,” in Proceedings of the 8th Joint Conference on Lexical and Computational Semantics, Minneapolis, MN, USA, 2019, pp. 1--8. [Online]. Available: https://www.aclweb.org/anthology/S19-1001/
  21. D. Schlechtweg, S. Schulte im Walde, and S. Eckmann, “Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change,” in Proceedings of the 2018 Conference of the North American Chapter  of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana, 2018, pp. 169--174. [Online]. Available: https://www.aclweb.org/anthology/N18-2027/
  22. 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), 2018.
  23. V. Shwartz, E. Santus, and D. Schlechtweg, “Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy                Detection,” in Proceedings of the 15th Conference of the European Chapter of the                Association for Computational Linguistics, Valencia,                Spain, 2017, pp. 65--75. [Online]. Available: https://www.aclweb.org/anthology/E17-1007/
  24. D. Schlechtweg, S. Eckmann, E. Santus, S. Schulte im Walde, and D. Hole, “German in Flux: Detecting Metaphoric Change via Word Entropy,” in Proceedings of the 21st Conference on Computational Natural Language Learning, Vancouver, Canada, 2017, pp. 354--367. [Online]. Available: https://www.aclweb.org/anthology/K17-1036/
  25. D. Schlechtweg, “Exploitation of Co-reference in Distributional Semantics,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia, 2016.
  26. H. van Lee and D. Schlechtweg, “Knowledge and Belief in Skat -- Modelling two kinds of information in the German cardgame Skat,” 2014.
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