Piotr Przybyła

Natural Language Processing Researcher

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About Me

I am an assistant professor in 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, HOMADOS (Hampering Misinformation by Assessing Credibility of Online Sources), is funded within the Polish Returns programme of the Polish National Agency for Academic Exchange.

Research

Interests

Text credibility

Methods for classifying, retrieving and understanding text based on its credibility.

User-aware NLP

Focusing on interpretability to build NLP solutions veriafiable through real-world user studies.

Meaning-preserving NLG

Natural language generation for text transformation preserving the meaning (simplification, style transfer).

Biomedical text processing

Text mining, information retrieval, systematic reviews automation, electronic health records analysis, etc.

... and more

author profiling, text classification, NLP of Polish, numerical methods for sciences, etc.

Past projects

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

Journal Articles

  1. 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]
  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. 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]
  8. 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]
  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]
  10. 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]
  11. 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]

Conference Proceedings

  1. 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]
  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]
  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. 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]
  5. 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]
  6. 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]
  7. 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]
  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. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. P. Przybyła, “Issues of Polish Question Answering,” in Proceedings of the first conference “Information Technologies: Research and their Interdisciplinary Applications” (ITRIA 2012), Warsaw, Poland, 2015.[bib][paper]

Presentations

  1. M. Le Pogam, P. Przybyła, E. Ojeda Ruiz, S. Bacher, S. Ananiadou, E. von Elm, “Improving efficiency of reference screening in systematic literature reviews using the RobotAnalyst text mining application: performance assessment in a systematic review on patient safety,” in Swiss Public Health Conference, Basel, Switzerland, 2017. [abstract]
  2. M. Le Pogam, P. Przybyła, E. Ojeda Ruiz, S. Bacher, S. Ananiadou, E. von Elm, “Using the RobotAnalyst text-mining application to boost efficiency of literature screening: experience from a systematic review in health services research,” in Global Evidence Summit, Cape Town, South Africa, 2017. [abstract]
  3. K. Nolan, S. Ananiadou, P. Przybyła, A. J. Brockmeier, “RobotAnalyst: An online system to support citation screening in evidence reviewing,” in Global Evidence Summit, Cape Town, South Africa, 2017. [abstract]
  4. K. Nolan, S. Ananiadou, M. Le Pogam, E. von Elm, P. Przybyła, “Screening evidence for systematic reviews using a text-mining system: The RobotAnalyst,” in Global Evidence Summit, Cape Town, South Africa, 2017. [abstract]
  5. G. Kontonatsios, R. Batista-Navarro, P. Przybyła, and S. Ananiadou, “Text mining methods to support the development of sensitive search strategies in public health reviews,” in Cochrane Colloquium 2016, Seoul, South Korea, 2016. [abstract]
  6. M. Shardlow, P. Przybyła, R. Batista-Navarro, J. Carter, J. McNaught, and S. Ananiadou, “Facilitating and promoting web annotation with Argo,” in I Annotate 2016, Berlin, Germany, 2016.[video]
  7. P. Przybyła, “OpenMinTeD -- Open Mining Infrastructure for Text and Data,” in 7th Plenary Meeting of Research Data Alliance (RDA) Poster Session, Tokyo, Japan, 2016.

Other

  1. 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]
  2. P. Przybyła, “Boosting Question Answering by Deep Entity Recognition,” Manuscript arXiv:1605.08675 [cs.CL], 2016.[bib][paper][data][corpus]
  3. 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]

See also on: DBLP, Google Scholar and ResearchGate.

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