Dynamic bayesian combination of multiple imperfect classifiers E Simpson, S Roberts, I Psorakis, A Smith Decision making and imperfection, 1-35, 2013 | 188 | 2013 |
Text processing like humans do: Visually attacking and shielding NLP systems S Eger, GG Şahin, A Rücklé, JU Lee, C Schulz, M Mesgar, K Swarnkar, ... arXiv preprint arXiv:1903.11508, 2019 | 168 | 2019 |
A disaster response system based on human-agent collectives SD Ramchurn, TD Huynh, F Wu, Y Ikuno, J Flann, L Moreau, JE Fischer, ... Journal of Artificial Intelligence Research 57, 661-708, 2016 | 130 | 2016 |
Space Warps – I. Crowdsourcing the discovery of gravitational lenses PJ Marshall, A Verma, A More, CP Davis, S More, A Kapadia, M Parrish, ... Monthly Notices of the Royal Astronomical Society 455 (2), 1171-1190, 2016 | 126 | 2016 |
Efficient methods for natural language processing: A survey M Treviso, JU Lee, T Ji, B Aken, Q Cao, MR Ciosici, M Hassid, K Heafield, ... Transactions of the Association for Computational Linguistics 11, 826-860, 2023 | 74 | 2023 |
Content-centered collaboration spaces in the cloud J Erickson, M Rhodes, S Spence, D Banks, J Rutherford, E Simpson, ... IEEE Internet computing 13 (5), 34-42, 2009 | 68 | 2009 |
Finding convincing arguments using scalable Bayesian preference learning E Simpson, I Gurevych Transactions of the Association for Computational Linguistics 6, 357-371, 2018 | 59 | 2018 |
Macdonald on the Law of Freedom of Information I Peacock, E Simpson, A Pay, S Adamyk, C Ford, A Littler, G McNicholas Oxford University Press, 2016 | 57 | 2016 |
Clustering tags in enterprise and web folksonomies E Simpson Proceedings of the International AAAI Conference on Web and Social Media 2 …, 2008 | 57 | 2008 |
Predicting economic indicators from web text using sentiment composition A Levenberg, S Pulman, K Moilanen, E Simpson, S Roberts International Journal of Computer and Communication Engineering 3 (2), 109-115, 2014 | 52 | 2014 |
SemEval-2021 task 12: Learning with disagreements A Uma, T Fornaciari, A Dumitrache, T Miller, J Chamberlain, B Plank, ... Proceedings of the 15th International Workshop on Semantic Evaluation …, 2021 | 47 | 2021 |
A Bayesian approach for sequence tagging with crowds E Simpson, I Gurevych arXiv preprint arXiv:1811.00780, 2018 | 42 | 2018 |
Bayesian methods for intelligent task assignment in crowdsourcing systems E Simpson, S Roberts Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, 1-32, 2015 | 38 | 2015 |
Language understanding in the wild: Combining crowdsourcing and machine learning ED Simpson, M Venanzi, S Reece, P Kohli, J Guiver, SJ Roberts, ... Proceedings of the 24th international conference on world wide web, 992-1002, 2015 | 35 | 2015 |
Scalable Bayesian preference learning for crowds E Simpson, I Gurevych Machine Learning 109 (4), 689-718, 2020 | 34 | 2020 |
Predicting humorousness and metaphor novelty with Gaussian process preference learning E Simpson, EL Do Dinh, T Miller, I Gurevych Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 31 | 2019 |
Improving factual consistency between a response and persona facts M Mesgar, E Simpson, I Gurevych arXiv preprint arXiv:2005.00036, 2020 | 28 | 2020 |
Low resource sequence tagging with weak labels E Simpson, J Pfeiffer, I Gurevych Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8862-8869, 2020 | 17 | 2020 |
Tag clustering with self organizing maps ML Sbodio, E Simpson HP Labs Techincal Reports, 2009 | 17 | 2009 |
Combined decision making with multiple agents ED Simpson University of Oxford, 2014 | 12 | 2014 |