| Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error A Bannach-Brown, P Przybyła, J Thomas, ASC Rice, S Ananiadou, J Liao, ... Systematic reviews 8 (1), 23, 2019 | 185* | 2019 |
| Capturing the Style of Fake News P Przybyla Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 490-497, 2020 | 158 | 2020 |
| Prioritising references for systematic reviews with RobotAnalyst: a user study P Przybyła, AJ Brockmeier, G Kontonatsios, MA Le Pogam, J McNaught, ... Research synthesis methods 9 (3), 470-488, 2018 | 124* | 2018 |
| Thalia: Semantic search engine for biomedical abstracts AJ Soto, P Przybyła, S Ananiadou Bioinformatics, 2018 | 70 | 2018 |
| Improving reference prioritisation with PICO recognition AJ Brockmeier, M Ju, P Przybyła, S Ananiadou BMC Medical Informatics and Decision Making 19 (1), 256, 2019 | 63 | 2019 |
| Text mining resources for the life sciences P Przybyła, M Shardlow, S Aubin, R Bossy, R Eckart de Castilho, ... Database 2016, baw145, 2016 | 58 | 2016 |
| A semi-supervised approach using label propagation to support citation screening G Kontonatsios, AJ Brockmeier, P Przybyła, J McNaught, T Mu, ... Journal of biomedical informatics 72, 67-76, 2017 | 53 | 2017 |
| The CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness A Barrón-Cedeño, F Alam, T Chakraborty, T Elsayed, P Nakov, P Przybyła, ... European Conference on Information Retrieval, 449-458, 2024 | 33 | 2024 |
| When classification accuracy is not enough: Explaining news credibility assessment P Przybyła, AJ Soto Information Processing & Management 58 (5), 102653, 2021 | 28 | 2021 |
| Overview of the CLEF-2024 CheckThat! Lab: Check-worthiness, subjectivity, persuasion, roles, authorities and adversarial robustness A Barrón-Cedeño, F Alam, JM Struß, P Nakov, T Chakraborty, T Elsayed, ... Experimental IR Meets Multilinguality, Multimodality, and Interaction …, 2024 | 20 | 2024 |
| The IPIPAN team participation in the check-worthiness task of the CLEF2019 CheckThat! Lab J Gąsior, P Przybyła | 14 | 2019 |
| I’ve Seen Things You Machines Wouldn’t Believe: Measuring Content Predictability to Identify Automatically-Generated Text P Przybyła, N Duran-Silva, S Egea-Gómez Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023). CEUR …, 2023 | 12 | 2023 |
| Boosting Question Answering by Deep Entity Recognition P Przybyła arXiv preprint arXiv:1605.08675, 2016 | 12 | 2016 |
| What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling. P Przybyła, P Teisseyre Proceedings of CLEF, 2015 | 12* | 2015 |
| BODEGA: Benchmark for Adversarial Example Generation in Credibility Assessment P Przybyła, A Shvets, H Saggion arXiv preprint arXiv:2303.08032, 2023 | 11* | 2023 |
| Multi-Word Lexical Simplification P Przybyła, M Shardlow Proceedings of the 28th International Conference on Computational …, 2020 | 11 | 2020 |
| Investigating Text Simplification Evaluation L Vásquez-Rodríguez, M Shardlow, P Przybyła, S Ananiadou arXiv preprint arXiv:2107.13662, 2021 | 10 | 2021 |
| Analysing utterances in polish parliament to predict speaker’s background P Przybyła, P Teisseyre Journal of Quantitative Linguistics 21 (4), 350-376, 2014 | 10 | 2014 |
| How big is big enough? Unsupervised word sense disambiguation using a very large corpus P Przybyła arXiv preprint arXiv:1710.07960, 2017 | 9 | 2017 |
| Detecting Bot Accounts on Twitter by Measuring Message Predictability P Przybyła CLEF 2019 Labs and Workshops, Notebook Papers, 2019 | 8 | 2019 |
