| DeepProbLog: Neural Probabilistic Logic Programming R Manhaeve, S Dumančić, A Kimmig, T Demeester, L De Raedt Conf. Neural Information Processing Systems (NIPS 2018) [spotlight presentation], 2018 | 485 | 2018 |
| Joint entity recognition and relation extraction as a multi-head selection problem G Bekoulis, J Deleu, T Demeester, C Develder Expert Systems with Applications, Volume 114, 30 December 2018, Pages 34-45, 2018 | 387 | 2018 |
| Representation learning for very short texts using weighted word embedding aggregation C De Boom, S Van Canneyt, T Demeester, B Dhoedt Pattern Recognition Letters 80, 150-156, 2016 | 247 | 2016 |
| Adversarial training for multi-context joint entity and relation extraction G Bekoulis, J Deleu, T Demeester, C Develder 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), 2018 | 187 | 2018 |
| Lifted rule injection for relation embeddings T Demeester, T Rocktäschel, S Riedel Conf. on Empirical Methods in Natural Language Processing (EMNLP 2016), 2016 | 167 | 2016 |
| Adversarial sets for regularising neural link predictors P Minervini, T Demeester, T Rocktäschel, S Riedel 33rd Conf. on Uncertainty in Artificial Intelligence (UAI 2017), 2017 | 113 | 2017 |
| Learning semantic similarity for very short texts C De Boom, S Van Canneyt, S Bohez, T Demeester, B Dhoedt 2015 ieee international conference on data mining workshop (icdmw), 1229-1234, 2015 | 102 | 2015 |
| A self-training approach for short text clustering A Hadifar, L Sterckx, T Demeester, C Develder Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP …, 2019 | 85 | 2019 |
| Neural probabilistic logic programming in DeepProbLog R Manhaeve, S Dumančić, A Kimmig, T Demeester, L De Raedt Artificial Intelligence 298, 103504, 2021 | 77 | 2021 |
| Topical word importance for fast keyphrase extraction L Sterckx, T Demeester, J Deleu, C Develder Proceedings of the 24th International Conference on World Wide Web, 121-122, 2015 | 77 | 2015 |
| Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling G Vandewiele, I Dehaene, G Kovács, L Sterckx, O Janssens, F Ongenae, ... Artificial Intelligence in Medicine 111, 101987, 2021 | 75* | 2021 |
| Quasi-TM transmission line parameters of coupled lossy lines based on the Dirichlet to Neumann boundary operator T Demeester, D De Zutter IEEE Transactions on Microwave Theory and Techniques 56 (7), 1649-1660, 2008 | 65 | 2008 |
| Taily: Shard Selection Using the Tail of Score Distributions R Aly, D Hiemstra, T Demeester SIGIR 2013, 2013 | 63 | 2013 |
| Supervised keyphrase extraction as positive unlabeled learning L Sterckx, T Demeester, C Develder, C Caragea EMNLP2016, the conference on empirical methods in natural language …, 2016 | 62 | 2016 |
| Overview of the trec 2014 federated web search track T Demeester, D Trieschnigg, D Nguyen, D Hiemstra, K Zhou Proceedings of The Twenty-Third Text REtrieval Conference, TREC 2014 …, 2014 | 55 | 2014 |
| Neuro-symbolic= neural+ logical+ probabilistic L De Raedt, R Manhaeve, S Dumancic, T Demeester, A Kimmig NeSy'19@ IJCAI, the 14th International Workshop on Neural-Symbolic Learning …, 2019 | 53 | 2019 |
| Federated Search in the Wild: The Combined Power of over a Hundred Search Engines D Nguyen, T Demeester, D Trieschnigg, D Hiemstra CIKM 2012, 1874-1878, 2012 | 53 | 2012 |
| DWIE: An entity-centric dataset for multi-task document-level information extraction K Zaporojets, J Deleu, C Develder, T Demeester Information Processing & Management 58 (4), 102563, 2021 | 45 | 2021 |
| Modeling and analysis of residential flexibility: Timing of white good usage N Sadeghianpourhamami, T Demeester, DF Benoit, M Strobbe, ... Applied energy 179, 790-805, 2016 | 36 | 2016 |
| An attentive neural architecture for joint segmentation and parsing and its application to real estate ads G Bekoulis, J Deleu, T Demeester, C Develder Expert Systems with Applications 102, 100-112, 2018 | 30 | 2018 |
Chris DevelderGhent University - imecVerified email at ugent.be
