TU Wien Informatics

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Gábor Recski

Univ.Ass. / PhD

Research Focus

  • Information Systems Engineering: 70%
  • Logic and Computation: 30%
Gábor Recski

About

Gábor Recski (he/him) is a computational linguist working in the field of natural language processing (NLP). Formerly an assistant professor at the Budapest University of Technology, he is currently a postdoctoral researcher in the Data Science Research Unit at the TU Wien Faculty of Informatics. His research interests include the computational modeling of natural language semantics and the use of such models for transparent information extraction. In addition to his research Gábor also teaches two courses on NLP at the Faculty and supervises several students at the BSc, MSc, and PhD levels. He is the co-author of over 50 peer-reviewed publications and a program committee member of ACL, EMNLP, LREC, as well as ACM CIKM and SIGIR.

Roles

  • PostDoc Researcher
    Data Science, E194-04
  • Faculty Council
    Substitute Member
  • Curriculum Commission for Business Informatics
    Substitute Member

Contact

2024W

2025S

 

  • Fact-checking LLMs with explainable information extraction / Recski, G. (2024, November 19). Fact-checking LLMs with explainable information extraction [Conference Presentation]. Language Intelligence 2024, Austria. https://doi.org/10.34726/8540
    Download: Slides (1.74 MB)
  • BRISE-plandok: a German legal corpus of building regulations / Recski, G., Iklodi, E., Lellmann, B., Kovács, Á., & Hanbury, A. (2024). BRISE-plandok: a German legal corpus of building regulations. Language Resources and Evaluation. https://doi.org/10.1007/s10579-024-09747-7
    Project: BRISE-Vienna (2019–2025)
  • TU Wien at SemEval-2024 Task 6: Unifying Model-Agnostic and Model-Aware Techniques for Hallucination Detection / Arzt, V., Azarbeik, M. M., Lasy, I., Kerl, T., & Recski, G. (2024). TU Wien at SemEval-2024 Task 6: Unifying Model-Agnostic and Model-Aware Techniques for Hallucination Detection. In A. K. Ojha, A. S. Dogruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, & A. Rosá (Eds.), Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) (pp. 1183–1196). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.semeval-1.173
  • Nyelvi sokszínűség az emberi és gépi fordításban / Recski, G. (2024, May 15). Nyelvi sokszínűség az emberi és gépi fordításban [Keynote Presentation]. Elektrubadúr plusz: Mesterséges intelligencia és műfordítás, Budapest, Hungary. http://hdl.handle.net/20.500.12708/199011
    Download: Slides (516 KB)
  • What can AI do for Advanced Legal Research? / Recski, G. (2024, February 16). What can AI do for Advanced Legal Research? [Conference Presentation]. IRIS24: Internationales Rechtsinformatik Symposion 2024, Salzburg, Austria. https://doi.org/10.34726/6140
    Download: Slides (1.31 MB)
  • TPPMI - a Temporal Positive Pointwise Mutual Information Embedding of Words / Schmitt, P., Rakovics, Z., Rakovics, M., & Recski, G. (2024). TPPMI - a Temporal Positive Pointwise Mutual Information Embedding of Words. In Proceedings of the 4th Workshop on Computational Linguistics for the Political and Social Sciences: Long and short papers (pp. 119–125). http://hdl.handle.net/20.500.12708/201681
  • Word alignment in Discourse Representation Structure parsing / Obereder, C., & Recski, G. (2024). Word alignment in Discourse Representation Structure parsing. In Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024) (pp. 50–56). http://hdl.handle.net/20.500.12708/201684
  • Natural Language Processing / Recski, G. (2023, November 10). Natural Language Processing [Keynote Presentation]. Fachtagung Spracherkennung, Wien, Austria.
  • What can AI do for Advanced Legal Research? / Recski, G. (2023, November 8). What can AI do for Advanced Legal Research? [Keynote Presentation]. Law via the Internet Conference 2023, Wien, Austria.
  • 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
    Download: PDF (335 KB)
  • 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. http://hdl.handle.net/20.500.12708/187885
  • 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
    Download: PDF (214 KB)
  • 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
    Download: PDF (1.15 MB)
    Project: BRISE-Vienna (2019–2025)
  • Transparent information extraction from natural language / Recski, G. (2022, June 22). Transparent information extraction from natural language [Presentation]. Complexity Science Hub talk, Austria.
    Download: slides (6.09 MB)
  • 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
    Download: PDF (256 KB)
  • 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. 16th Conference of the European Chapter of the Association for Computational Linguistics, Unknown. 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
  • 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
  • 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
  • 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
  • Explainable lexical entailment with semantic graphs / Kovacs, A., Gemes, K., Kornai, A., & Recski, G. (2020). Explainable lexical entailment with semantic graphs. In Proceedings of the 14th International Workshop on Semantic Evaluation (pp. 135–141). ACL Anthology. http://hdl.handle.net/20.500.12708/58391
  • 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