Piotr Przybyła

Natural Language Processing Researcher

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I am working as a postdoctoral researcher in the TALN (Natural Language Processing) Research Group at the Universitat Pompeu Fabra in Barcelona, Spain. I am also affiliated with the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences (ICS PAS) in Warsaw, Poland. Before that I obtained my PhD degree in Computer Science from ICS PAS and worked as a research fellow in the National Centre for Text Mining (NaCTeM) at the University of Manchester.

My current research project, ERINIA (Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions), is funded as a Marie Skłodowska-Curie Postdoctoral Fellowship by the European Union.

In case you're wondering: my surname is pronounced /pʂɨbɨwa/, as in: Powerful sheikh is bringing in wonderful art.

News

Past projects

HOMADOS

Hampering Misinformation by Assessing Credibility of Online Sources

MOIRA

Medical Evidence Interpretation for Risk Assessment

OpenMinTeD

Open Mining Infrastructure for Text & Data

Big Mechanism

DARPA Big Mechanism programme: cancer research

Mining4EBPH

Supporting Evidence-based Public Health Interventions using Text Mining

SLiM

Pilot study of the utility of text mining and machine learning tools to accelerate systematic review and meta-analysis of findings of in vivo research

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. 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]
  2. P. Przybyła, A. Shvets, H. Saggion, “Verifying the Robustness of Automatic Credibility Assessment,” Manuscript arXiv:2303.08032 [cs.CL], 2023.[bib][paper][code]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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 text

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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. 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]
  2. M. Ogrodniczuk, P. Przybyła, “PolEval 2021 Task 4: Question Answering Challenge,” in Proceedings of the PolEval 2021 Workshop, Online, 2021. [bib][paper][data]
  3. 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]
  4. P. Przybyła, “Boosting Question Answering by Deep Entity Recognition,” Manuscript arXiv:1605.08675 [cs.CL], 2016.[bib][paper][data][corpus]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]

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