| 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 | 157* | 2019 |
| Capturing the Style of Fake News P Przybyla Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 490-497, 2020 | 123 | 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 | 100 | 2018 |
| Thalia: Semantic search engine for biomedical abstracts AJ Soto, P Przybyła, S Ananiadou Bioinformatics, 2018 | 64 | 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 | 50 | 2019 |
| Text mining resources for the life sciences P Przybyła, M Shardlow, S Aubin, R Bossy, R Eckart de Castilho, ... Database 2016, baw145, 2016 | 50 | 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 | 49 | 2017 |
| When classification accuracy is not enough: Explaining news credibility assessment P Przybyła, AJ Soto Information Processing & Management 58 (5), 102653, 2021 | 23 | 2021 |
| The IPIPAN team participation in the check-worthiness task of the CLEF2019 CheckThat! Lab J Gąsior, P Przybyła | 14 | 2019 |
| What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling. P Przybyła, P Teisseyre Proceedings of CLEF, 2015 | 13* | 2015 |
| Boosting Question Answering by Deep Entity Recognition P Przybyła arXiv preprint arXiv:1605.08675, 2016 | 11 | 2016 |
| How big is big enough? Unsupervised word sense disambiguation using a very large corpus P Przybyła arXiv preprint arXiv:1710.07960, 2017 | 9 | 2017 |
| Investigating Text Simplification Evaluation L Vásquez-Rodríguez, M Shardlow, P Przybyła, S Ananiadou arXiv preprint arXiv:2107.13662, 2021 | 8 | 2021 |
| Detecting Bot Accounts on Twitter by Measuring Message Predictability P Przybyła CLEF 2019 Labs and Workshops, Notebook Papers, 2019 | 8 | 2019 |
| NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features P Przybyła, NTH Nguyen, M Shardlow, G Kontonatsios, S Ananiadou Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016 | 8 | 2016 |
| Analysing utterances in polish parliament to predict speaker’s background P Przybyła, P Teisseyre Journal of Quantitative Linguistics 21 (4), 350-376, 2014 | 8 | 2014 |
| Question analysis for Polish question answering P Przybyła 51st Annual Meeting of the Association for Computational Linguistics …, 2013 | 8 | 2013 |
| Multi-Word Lexical Simplification P Przybyła, M Shardlow Proceedings of the 28th International Conference on Computational …, 2020 | 7 | 2020 |
| Quantifying risk factors in medical reports with a context-aware linear model P Przybyła, AJ Brockmeier, S Ananiadou Journal of the American Medical Informatics Association 26 (6), 537-546, 2019 | 7 | 2019 |
| The Role of Text Simplification Operations in Evaluation L Vásquez-Rodríguez, M Shardlow, P Przybyła, S Ananiadou The First Workshop on Current Trends in Text Simplification (CTTS 2021), co …, 2021 | 6 | 2021 |
