Edwin Simpson
Sitert av
Sitert av
Dynamic bayesian combination of multiple imperfect classifiers
E Simpson, S Roberts, I Psorakis, A Smith
Decision making and imperfection, 1-35, 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
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
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
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
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
Finding convincing arguments using scalable Bayesian preference learning
E Simpson, I Gurevych
Transactions of the Association for Computational Linguistics 6, 357-371, 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
Clustering tags in enterprise and web folksonomies
E Simpson
Proceedings of the International AAAI Conference on Web and Social Media 2 …, 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
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
A Bayesian approach for sequence tagging with crowds
E Simpson, I Gurevych
arXiv preprint arXiv:1811.00780, 2018
Bayesian methods for intelligent task assignment in crowdsourcing systems
E Simpson, S Roberts
Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, 1-32, 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
Scalable Bayesian preference learning for crowds
E Simpson, I Gurevych
Machine Learning 109 (4), 689-718, 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
Improving factual consistency between a response and persona facts
M Mesgar, E Simpson, I Gurevych
arXiv preprint arXiv:2005.00036, 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
Tag clustering with self organizing maps
ML Sbodio, E Simpson
HP Labs Techincal Reports, 2009
Combined decision making with multiple agents
ED Simpson
University of Oxford, 2014
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Artikler 1–20