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Piotr Przybyła
Piotr Przybyła
TALN, Universitat Pompeu Fabra / Institute of Computer Science, Polish Academy of Sciences
Verified email at upf.edu
Title
Cited by
Cited by
Year
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
153*2019
Capturing the Style of Fake News
P Przybyla
Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 490-497, 2020
1132020
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
1002018
Thalia: Semantic search engine for biomedical abstracts
AJ Soto, P Przybyła, S Ananiadou
Bioinformatics, 2018
642018
Text mining resources for the life sciences
P Przybyła, M Shardlow, S Aubin, R Bossy, R Eckart de Castilho, ...
Database 2016, baw145, 2016
502016
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
492017
Improving reference prioritisation with PICO recognition
AJ Brockmeier, M Ju, P Przybyła, S Ananiadou
BMC Medical Informatics and Decision Making 19 (1), 256, 2019
472019
When classification accuracy is not enough: Explaining news credibility assessment
P Przybyła, AJ Soto
Information Processing & Management 58 (5), 102653, 2021
232021
The IPIPAN team participation in the check-worthiness task of the CLEF2019 CheckThat! Lab
J Gąsior, P Przybyła
142019
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
112016
How big is big enough? Unsupervised word sense disambiguation using a very large corpus
P Przybyła
arXiv preprint arXiv:1710.07960, 2017
92017
Investigating Text Simplification Evaluation
L Vásquez-Rodríguez, M Shardlow, P Przybyła, S Ananiadou
arXiv preprint arXiv:2107.13662, 2021
82021
Detecting Bot Accounts on Twitter by Measuring Message Predictability
P Przybyła
CLEF 2019 Labs and Workshops, Notebook Papers, 2019
82019
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
82016
Analysing utterances in polish parliament to predict speaker’s background
P Przybyła, P Teisseyre
Journal of Quantitative Linguistics 21 (4), 350-376, 2014
82014
Question analysis for Polish question answering
P Przybyła
51st Annual Meeting of the Association for Computational Linguistics …, 2013
82013
Multi-Word Lexical Simplification
P Przybyła, M Shardlow
Proceedings of the 28th International Conference on Computational …, 2020
72020
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
72019
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
62021
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