| 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 | 43* | 2019 |
| 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 | 38 | 2018 |
| Text mining resources for the life sciences P Przybyła, M Shardlow, S Aubin, R Bossy, R Eckart de Castilho, ... Database 2016, 2016 | 22 | 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 | 21 | 2017 |
| Thalia: Semantic search engine for biomedical abstracts AJ Soto, P Przybyła, S Ananiadou Bioinformatics, 2018 | 16 | 2018 |
| What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling. P Przybyła, P Teisseyre Proceedings of CLEF, 2015 | 10* | 2015 |
| The IPIPAN Team Participation in the Check-Worthiness Task of the CLEF2019 CheckThat! Lab. J Gasior, P Przybyla CLEF (Working Notes), 2019 | 9 | 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 | 5 | 2016 |
| Question analysis for Polish question answering P Przybyła 51st Annual Meeting of the Association for Computational Linguistics …, 2013 | 5 | 2013 |
| Question classification for Polish question answering P Przybyła Language Processing and Intelligent Information Systems, 50-56, 2013 | 5 | 2013 |
| Improving reference prioritisation with PICO recognition AJ Brockmeier, M Ju, P Przybyła, S Ananiadou BMC Medical Informatics and Decision Making 19 (1), 256, 2019 | 4 | 2019 |
| How big is big enough? Unsupervised word sense disambiguation using a very large corpus P Przybyła arXiv preprint arXiv:1710.07960, 2017 | 4 | 2017 |
| Analysing utterances in polish parliament to predict speaker’s background P Przybyła, P Teisseyre Journal of Quantitative Linguistics 21 (4), 350-376, 2014 | 4 | 2014 |
| Issues of Polish Question Answering P Przybyła Proceedings of the first conference’Information Technologies: Research and …, 2012 | 4 | 2012 |
| Generalisation of the identity method for determination of high-order moments of multiplicity distributions with a software implementation M Maćkowiak-Pawłowska, P Przybyła The European Physical Journal C 78 (5), 1-8, 2018 | 3 | 2018 |
| Boosting Question Answering by Deep Entity Recognition P Przybyła arXiv preprint arXiv:1605.08675, 2016 | 3 | 2016 |
| Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia. P Przybyla, AJ Soto, S Ananiadou TREC, 2017 | 2 | 2017 |
| Capturing the Style of Fake News P Przybyla Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 490-497, 2020 | 1 | 2020 |
| Detecting Bot Accounts on Twitter by Measuring Message Predictability P Przybyła | 1 | 2019 |
| Gathering Knowledge for Question Answering Beyond Named Entities P Przybyła International Conference on Applications of Natural Language to Information …, 2015 | 1 | 2015 |
