| A systematic review of natural language processing applied to radiology reports A Casey, E Davidson, M Poon, H Dong, D Duma, A Grivas, C Grover, ... BMC medical informatics and decision making 21 (1), 1-18, 2021 | 112 | 2021 |
| What do character-level models learn about morphology? The case of dependency parsing C Vania, A Grivas, A Lopez arXiv preprint arXiv:1808.09180, 2018 | 33 | 2018 |
| Author Profiling using Stylometric and Structural Feature Groupings. A Grivas, A Krithara, G Giannakopoulos CLEF (Working Notes), 2015 | 31 | 2015 |
| Comparison of rule-based and neural network models for negation detection in radiology reports D Sykes, A Grivas, C Grover, R Tobin, C Sudlow, W Whiteley, A McIntosh, ... Natural Language Engineering 27 (2), 203-224, 2021 | 27 | 2021 |
| Not a cute stroke: analysis of rule-and neural network-based information extraction systems for brain radiology reports A Grivas, B Alex, C Grover, R Tobin, W Whiteley Proceedings of the 11th international workshop on health text mining and …, 2020 | 25 | 2020 |
| PaloPro: a platform for knowledge extraction from big social data and the news N Makrynioti, A Grivas, C Sardianos, N Tsirakis, I Varlamis, V Vassalos, ... International Journal of Big Data Intelligence 4 (1), 3-22, 2017 | 12 | 2017 |
| Low-rank softmax can have unargmaxable classes in theory but rarely in practice A Grivas, N Bogoychev, A Lopez arXiv preprint arXiv:2203.06462, 2022 | 7 | 2022 |
| Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification A Grivas, A Vergari, A Lopez Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12208 …, 2024 | 1 | 2024 |
| Parsing morphologically-rich languages using neural networks A Grivas | | |
