Piotr Przybyła

News

• (08.2024) At this year's ACL, I'm presenting our work on the adaptive search for adversarial examples (at the WASSA workshop) and processing affiliations in scholarly literature (at the SDProc workshop).
• (08.2024) On the 20th of August, I'll be presenting our work in the ERINIA project at a seminar at the Chulalongkorn University in Bangkok.
• (08.2024) The results of our InCrediblAE Task 6 at CheckThat! 2024 lab have been published in the overview article.
• (05.2024) Our work on a Polish QA corpus has been presented at LREC-COLING 2024 in Torino, Italy. [paper]
• (03.2024) An article describing the CheckThat! 2024 lab, including our Task 6, has been published in the proceedings of ECIR 2024. [paper]
• (03.2024) I am presenting BODEGA at the Natural Language Processing Seminar at Polish Academy of Sciences. The recording (in Polish) will be available through [website]
• (03.2024) My multi-level segmenter, LAMBO, has been updated to better deal with foreign characters and work with more languages: it supports 67 now! [code]
• (02.2024) A publication introducing a dataset for Polish QA has been accepted for LREC-COLING 2024. [paper]
• (01.2024) I am coordinating a shared task on Robustness of Credibility Assessment with Adversarial Examples (InCrediblAE): Task 6 at the CheckThat! 2024 Evaluation Lab. [website]
• (01.2024) I have been awarded a computing grant of 10,000 hours on the Athena supercomputer at AGH University of Kraków to accelerate work in the ERINIA project.
• (10.2023) An article describing our (winning!) solution to AuTexTification shared task has been published in IberLEF 2023 proceedings. [paper]
• (10.2023) An article describing a model for propaganda detection created within the HOMADOS project has been published in SEPLN 2023 proceedings. [paper]

Past projects

Publications

Detecting machine-generated text

1. P. Przybyła, N. Duran-Silva, S. Egea-Gómez, “I've Seen Things You Machines Wouldn't Believe: Measuring Content Predictability to Identify Automatically-Generated Text,” in Proceedings of the 5th Workshop on Iberian Languages Evaluation Forum (IberLEF 2023), Jaén, Spain, 2023. [bib][paper][code]
2. P. Przybyła, “Detecting Bot Accounts on Twitter by Measuring Message Predictability,” in Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, 2019. [bib][paper][code]

Credibility and misinformation

1. P. Przybyła, E. McGill, H. Saggion, “Know Thine Enemy: Adaptive Attacks on Misinformation Detection Using Reinforcement Learning,” in Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, Bangkok, Thailand, 2024. [bib][paper][code]
2. P. Przybyła, B. Wu, A. Shvets, Y. Mu, K. C. Sheang, X. Song, H. Saggion, “Overview of the CLEF-2024 CheckThat! Lab Task 6 on Robustness of Credibility Assessment with Adversarial Examples (InCrediblAE),” in Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum, Grenoble, France, 2024. [bib][paper][event][code]
3. A. Barrón-Cedeño, F. Alam, T. Chakraborty, T. Elsayed, P. Nakov, P. Przybyła, J. M. Struß, F. Haouari, M. Hasanain, F. Ruggeri, X. Song, R. Suwaileh, “The CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness,” in Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024), Glasgow, UK, 2024. [bib][paper][preprint][event]
4. P. Przybyła, A. Shvets, H. Saggion, “Verifying the Robustness of Automatic Credibility Assessment,” Manuscript arXiv:2303.08032 [cs.CL], 2023. [bib][paper][code]
5. P. Przybyła, K. Kaczyński, “Where Does It End? Long Named Entity Recognition for Propaganda Detection and Beyond,” in Proceedings of the Workshop on NLP applied to Misinformation co-located with 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), Jaén, Spain, 2023. [bib][paper][code]
6. P. Przybyła, H. Saggion, “ERINIA: Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions,” in Proceedings of the Workshop on NLP applied to Misinformation co-located with 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), Jaén, Spain, 2023. [bib][paper]
7. P. Przybyła, P. Borkowski, K. Kaczyński, “Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach,” in Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022. [bib][paper][preprint][data][corpus][code]
8. P. Przybyła, A. J. Soto, “When classification accuracy is not enough: Explaining news credibility assessment,” Information Processing & Management, vol. 58, issue 5, 2021.[bib][paper][data,code]
9. K. Kaczyński, P. Przybyła, “HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection,” in Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), Bangkok, Thailand, 2021. [bib][paper]
10. P. Przybyła, “Capturing the Style of Fake News,” in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, USA, 2020. [bib][paper][corpus][code]
11. J. Gąsior and P. Przybyła, “The IPIPAN Team Participation in the Check-Worthiness Task of the CLEF2019 CheckThat! Lab,” in Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, 2019.[bib][paper]

NLP meta-research

1. M. Shardlow, P. Przybyła, “Deanthropomorphising NLP: Can a Language Model Be Conscious?,” Manuscript arXiv:2211.11483 [cs.CL], 2022.[bib][paper]
2. P. Przybyła, M. Shardlow, “Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences,” in Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, 2022. [bib][paper][data][code]

Text simplification

1. M. Shardlow, P. Przybyła, “Simplification by Lexical Deletion,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code]
2. L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Document-level Text Simplification with Coherence Evaluation,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code]
3. L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “The Role of Text Simplification Operations in Evaluation,” in Proceedings of the First Workshop on Current Trends in Text Simplification (CTTS 2021), Online, 2021. [bib][paper][code]
4. L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Investigating Text Simplification Evaluation,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Bangkok, Thailand, 2021. [bib][paper][code]
5. P. Przybyła, M. Shardlow, “Multi-Word Lexical Simplification,” in Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain, 2020. [bib][paper][data][model][code]

NLP applications for biomedical and scholarly text

1. N. Duran-Silva, P. Accuosto, P. Przybyła, H. Saggion, “AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis,” in Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), Bangkok, Thailand, 2024. [bib][paper][code+data]
2. A. J. Brockmeier, M. Ju, P. Przybyła, S. Ananiadou, “Improving reference prioritisation with PICO recognition,” BMC Medical Informatics and Decision Making, vol. 19, p. 256, 2019. [bib][paper]
3. P. Przybyła, A. J. Brockmeier, S. Ananiadou, “Quantifying risk factors in medical reports with a context-aware linear model,” Journal of the American Medical Informatics Association, vol. 26, issue 6, pp. 537-546, 2019. [bib][paper]
4. A. Bannach-Brown, P. Przybyła, J. Thomas, A. S. C. Rice, S. Ananiadou, J. Liao, M. R. Macleod, “Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error,” Systematic Reviews, vol. 8, issue 1, pp. 23, 2019. [bib][paper][data][software]
5. A. J. Soto, P. Przybyła, S. Ananiadou, “Thalia: Semantic search engine for biomedical abstracts,” Bioinformatics, vol. 35, issue 10, pp. 1799-1801, 2018.[bib][paper][web service]
6. P. Przybyła, A. J. Brockmeier, G. Kontonatsios, M. Le Pogam, J. McNaught, E. von Elm, K. Nolan, S. Ananiadou, “Prioritising references for systematic reviews with RobotAnalyst: A user study,” Research Synthesis Methods, vol. 9, no. 3, pp. 470-488, 2018.[bib][paper][web service]
7. G. Kontonatsios, A. J. Brockmeier, P. Przybyła, J. McNaught, T. Mu, J. Y. Goulermas, S. Ananiadou, “A semi-supervised approach using label propagation to support citation screening,” Journal of Biomedical Informatics, vol. 72, 2017.[bib][paper]
8. P. Przybyła, A. J. Soto and S. Ananiadou, “Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia,” in Proceedings of the Twenty-Fifth Text REtrieval Conference (TREC 2017), Gaithersburg, Maryland, USA, 2017.[bib][paper]
9. P. Przybyła, M. Shardlow, S. Aubin, R. Bossy, R. Eckart de Castilho, S. Piperidis, J. McNaught, S. Ananiadou, “Text Mining Resources for the Life Sciences,” Database: The Journal of Biological Databases and Curation, vol. 2016, 2016.[bib][paper]

NLP for Polish

1. P. Rybak, P. Przybyła, M. Ogrodniczuk, “PolQA: Polish Question Answering Dataset,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy, 2024. [bib][paper][corpus]
2. Ł. Kobyliński, M. Ogrodniczuk, P. Rybak, P. Przybyła, P. Pęzik, A. Mikołajczyk, W. Janowski, M. Marcińczuk, A. Smywiński-Pohl, “PolEval 2022/23 Challenge Tasks and Results,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems (FedCSIS 2023), Warsaw, Poland, 2023. [bib][paper]
3. M. Ogrodniczuk, P. Przybyła, “PolEval 2021 Task 4: Question Answering Challenge,” in Proceedings of the PolEval 2021 Workshop, Online, 2021. [bib][paper][data]
4. P. Przybyła, “How big is big enough? Unsupervised word sense disambiguation using a very large corpus,” Manuscript arXiv:1710.07960 [cs.CL], 2017.[bib][paper]
5. P. Przybyła, “Boosting Question Answering by Deep Entity Recognition,” Manuscript arXiv:1605.08675 [cs.CL], 2016.[bib][paper][data][corpus]
6. P. Przybyła, “Odpowiadanie na pytania w języku polskim z użyciem głębokiego rozpoznawania nazw,” (Question Answering in Polish using Deep Entity Recognition), PhD thesis in Institute of Computer Science, Polish Academy of Sciences in Warsaw, Poland, 2015.[bib][paper][data][corpus]
7. P. Przybyła, “Gathering Knowledge for Question Answering Beyond Named Entities,” in Proceedings of the 20th International Conference on Applications of Natural Language to Information Systems (NLDB 2015), Passau, Germany, 2015.[bib][paper][data][corpus]
8. P. Przybyła and P. Teisseyre, “Analysing Utterances in Polish Parliament to Predict Speaker’s Background,” Journal of Quantitative Linguistics, vol. 21, no. 4, pp. 350–376, 2014.[bib][paper]
9. P. Przybyła, “Question Analysis for Polish Question Answering,” in 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop, Sofia, Bulgaria, 2013.[bib][paper]
10. P. Przybyła, “Question Classification for Polish Question Answering,” in Proceedings of the 20th International Conference on Language Processing and Intelligent Information Systems (LP&IIS 2013), Warsaw, Poland, 2013.[bib][paper]
11. P. Przybyła, “Issues of Polish Question Answering,” in Proceedings of the first conference “Information Technologies: Research and their Interdisciplinary Applications” (ITRIA 2012), Warsaw, Poland, 2012.[bib][paper]

Other NLP

1. P. Przybyła, N. T. H. Nguyen, M. Shardlow, G. Kontonatsios, and S. Ananiadou, “NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features,” in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, USA, 2016.[bib][paper]
2. P. Przybyła and P. Teisseyre, “What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling - Notebook for PAN at CLEF 2015,” in CLEF 2015 Labs and Workshops, Notebook Papers, Toulouse, France, 2015.[bib][paper]

Computations in physics

1. M. Maćkowiak-Pawłowska, P. Przybyła, “Generalisation of the identity method for determination of high-order moments of multiplicity distributions with a software implementation,” European Physical Journal C, vol. 78, issue 5, 2018.[bib][paper][software]
2. P. Przybyła, “A pattern recognition method for lattice distortion measurement from HRTEM images,” Journal of Microscopy, vol. 245, no. 2, pp. 200–209, 2011.[bib][paper]