| Learning relatedness measures for entity linking D Ceccarelli, C Lucchese, S Orlando, R Perego, S Trani Proceedings of the 22nd ACM international conference on Information …, 2013 | 119 | 2013 |
| Dexter: an open source framework for entity linking D Ceccarelli, C Lucchese, S Orlando, R Perego, S Trani Proceedings of the sixth international workshop on Exploiting semantic …, 2013 | 100 | 2013 |
| Post-learning optimization of tree ensembles for efficient ranking C Lucchese, FM Nardini, S Orlando, R Perego, F Silvestri, S Trani Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 79 | 2016 |
| Dexter 2.0: an open source tool for semantically enriching data S Trani, D Ceccarelli, C Lucchese, S Orlando, R Perego Proceedings of the 2014 International Conference on Posters & Demonstrations …, 2014 | 40 | 2014 |
| X-DART: blending dropout and pruning for efficient learning to rank C Lucchese, FM Nardini, S Orlando, R Perego, S Trani Proceedings of the 40th International ACM SIGIR Conference on Research and …, 2017 | 27 | 2017 |
| SEL: A unified algorithm for entity linking and saliency detection S Trani, D Ceccarelli, C Lucchese, S Orlando, R Perego Proceedings of the 2016 ACM Symposium on Document Engineering, 85-94, 2016 | 23 | 2016 |
| Selective gradient boosting for effective learning to rank C Lucchese, FM Nardini, R Perego, S Orlando, S Trani The 41st International ACM SIGIR Conference on Research & Development in …, 2018 | 22 | 2018 |
| SEL: A unified algorithm for salient entity linking S Trani, C Lucchese, R Perego, DE Losada, D Ceccarelli, S Orlando Computational Intelligence 34 (1), 2-29, 2018 | 20 | 2018 |
| Rankeval: An evaluation and analysis framework for learning-to-rank solutions C Lucchese, CI Muntean, FM Nardini, R Perego, S Trani Proceedings of the 40th international ACM SIGIR conference on research and …, 2017 | 19 | 2017 |
| Distilled neural networks for efficient learning to rank FM Nardini, C Rulli, S Trani, R Venturini IEEE Transactions on Knowledge and Data Engineering 35 (5), 4695-4712, 2022 | 17 | 2022 |
| X-CLEAVER: Learning Ranking Ensembles by Growing and Pruning Trees C Lucchese, FM Nardini, S Orlando, R Perego, F Silvestri, S Trani ACM Transactions on Intelligent Systems and Technology (TIST) 9 (6), 62, 2018 | 16 | 2018 |
| Efficient traversal of decision tree ensembles with FPGAs R Molina, F Loor, V Gil-Costa, FM Nardini, R Perego, S Trani Journal of Parallel and Distributed Computing 155, 38-49, 2021 | 9 | 2021 |
| Learning early exit strategies for additive ranking ensembles F Busolin, C Lucchese, FM Nardini, S Orlando, R Perego, S Trani Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 8 | 2021 |
| Query-level early exit for additive learning-to-rank ensembles C Lucchese, FM Nardini, S Orlando, R Perego, S Trani Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 8 | 2020 |
| Manual annotation of semi-structured documents for entity-linking S Trani, D Ceccarelli, C Lucchese, S Orlando, R Perego Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 8 | 2014 |
| The impact of negative samples on learning to rank C Lucchese, FM Nardini, R Perego, S Trani CEUR-WS. org, 2017 | 7 | 2017 |
| Ensemble model compression for fast and energy-efficient ranking on FPGAs V Gil-Costa, F Loor, R Molina, FM Nardini, R Perego, S Trani European Conference on Information Retrieval, 260-273, 2022 | 5 | 2022 |
| RankEval: Evaluation and investigation of ranking models C Lucchese, CI Muntean, FM Nardini, R Perego, S Trani SoftwareX 12, 100614, 2020 | 5 | 2020 |
| Early Exit Strategies for Learning-to-Rank Cascades F Busolin, C Lucchese, FM Nardini, S Orlando, R Perego, S Trani IEEE Access, 2023 | 2 | 2023 |
| Neural Network Compression using Binarization and Few Full-Precision Weights FM Nardini, C Rulli, S Trani, R Venturini arXiv preprint arXiv:2306.08960, 2023 | 1 | 2023 |
Raffaele PeregoResearch Director, ISTI-CNRVerified email at isti.cnr.it
