2023W

• Data-oriented Programming Paradigms / 188.995 / VU
• Introduction to Information Retrieval / 188.977 / VU
• Natural Language Processing and Information Extraction / 194.093 / VU
• Programming in Python / 194.123 / VU
• Research Seminar for Ph.D. Students / 188.423 / SE
• Research topics in natural language processing / 194.135 / VU

• Digital Humanism for Conversational AI
  2022 – 2023 / Vienna Business Agency (WAW)
• OPC UA Rule Editor 2.0
  2022 / Siemens AG

• Offensive text detection across languages and datasets using rule-based and hybrid methods / Gemes, K. A., Kovacs, A., & Recski, G. (2023). Offensive text detection across languages and datasets using rule-based and hybrid methods. In G. Drakopoulos & E. Kafeza (Eds.), CIKM-WS 2022. Proceedings of the CIKM 2022 Workshops. CEUR-WS.org. https://doi.org/10.34726/4341
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• Language complexity in human and machine translation: a preliminary study / Recski, G., & Kádár, F. (2023). Language complexity in human and machine translation: a preliminary study. In C. Orasan, R. Mitkov, G. Corpas Pastor, & J. Monti (Eds.), International Conference on Human-Informed Translation and Interpreting Technology (HiT-IT 2023). Proceedings (pp. 268–281). Incoma Ltd.
• Offensive Text Detection Across Languages and Datasets Using Rule-based and Hybrid Methods / Gemes, K. A., Kovacs, A., & Recski, G. (2022, October 21). Offensive Text Detection Across Languages and Datasets Using Rule-based and Hybrid Methods [Poster Presentation]. Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI), Atlanta, US-GA, United States of America (the). https://doi.org/10.34726/3742
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• POTATO: exPlainable infOrmation exTrAcTion framewOrk / Kovacs, A., Gémes, K., Iklódi, E., & Recski, G. (2022). POTATO: exPlainable infOrmation exTrAcTion framewOrk. In CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4897–4901). Association for Computing Machinery (ACM). https://doi.org/10.1145/3511808.3557196
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  Project: BRISE-Vienna (2019–2023)
• Transparent information extraction from natural language / Recski, G. (2022, June 22). Transparent information extraction from natural language [Presentation]. Complexity Science Hub talk, Austria.
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• Explainable lexical entailment with semantic graphs / Kovacs, A., Gemes, K., Kornai, A., & Recski, G. (2022). Explainable lexical entailment with semantic graphs. Natural Language Engineering, 1–24. https://doi.org/10.1017/s1351324922000092
• Offensive text detection on English Twitter with deep learning models and rule-based systems / Gemes, K. A., Kovacs, A., Reichel, M., & Recski, G. (2021). Offensive text detection on English Twitter with deep learning models and rule-based systems. In P. Mehta, T. Mandl, P. Majumder, & M. Mitra (Eds.), FIRE-WN 2021 [FIRE 2021 Working Notes] (pp. 283–296). CEUR-WS.org. https://doi.org/10.34726/4342
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• Explainable emotion detection with syntactic and semantic graphs / Recski, G. (2021). Explainable emotion detection with syntactic and semantic graphs. Österreichisches Treffen zu Sentimentinferenz (ÖTSI), Wien, Austria. http://hdl.handle.net/20.500.12708/87245
• Explainable Rule Extraction via Semantic Graphs / Recski, G., Lellmann, B., Kovacs, A., & Hanbury, A. (2021). Explainable Rule Extraction via Semantic Graphs. In Proceedings of the Fifth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2021) (pp. 24–35). CEUR-WS.org. http://hdl.handle.net/20.500.12708/58465
• The Gutenberg Dialogue Dataset / Csaky, R., & Recski, G. (2021). The Gutenberg Dialogue Dataset. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, Vancouver, Canada, Non-EU. The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.eacl-main.11
• DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets / Bogensperger, J., Schlarb, S., Hanbury, A., & Recski, G. (2021). DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.wnut-1.17
• TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments / Gemes, K. A., & Recski, G. (2021). TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments. In Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021 (pp. 69–75). netlibrary. https://doi.org/10.48415/2021/fhw5-x128
• Explainable lexical entailment with semantic graphs / Kovacs, A., Gemes, K., Kornai, A., & Recski, G. (2020). Explainable lexical entailment with semantic graphs. In Natural Language Engineering (pp. 1–24). ACL Anthology. https://doi.org/10.1017/s1351324922000092
• BME-TUW at SR'20: Lexical grammar induction for surface realization / Recski, G., Kovacs, A., Gemes, K. A., Ács, J., & Kornai, A. (2020). BME-TUW at SR’20: Lexical grammar induction for surface realization. In Proceedings of the Third Workshop on Multilingual Surface Realisation (MSR´20) (pp. 21–29). http://hdl.handle.net/20.500.12708/55594
• Machine comprehension using semantic graphs / Gemes, K. A., Kovacs, A., & Recski, G. (2019). Machine comprehension using semantic graphs. In Proceedings of the Automation and Applied Computer Science Workshop 2019. AACS. http://hdl.handle.net/20.500.12708/58793

• Transforming text annotations into graph-based features for a human-in-the-loop explainable information extraction framework / Chytilek, F. (2023). Transforming text annotations into graph-based features for a human-in-the-loop explainable information extraction framework [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.112080
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• Graph-based methods for user intent classification / Kurteshi, M. (2023). Graph-based methods for user intent classification [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105781
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• Commonsense question answering using hybrid models for efficiency and explainability / Breiner, G. (2023). Commonsense question answering using hybrid models for efficiency and explainability [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.102461
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• Explainability in hate speech detection / Reichel, M. (2022). Explainability in hate speech detection [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.91421
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• Exploring transfer learning techniques for named Entity recognition in Nnoisy user-generated text / Bogensperger, J. (2021). Exploring transfer learning techniques for named Entity recognition in Nnoisy user-generated text [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.86900
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