no code implementations • 7 Feb 2022 • Carlos Mougan, George Kanellos, Johannes Micheler, Jose Martinez, Thomas Gottron
For this approach we make use of explainable supervised machine learning to (a) identify the types of exceptions and (b) to prioritize which exceptions are more likely to require an intervention or correction by the NCBs.
no code implementations • 18 Jul 2021 • Carlos Mougan, Georgios Kanellos, Thomas Gottron
Explainable AI constitutes a fundamental step towards establishing fairness and addressing bias in algorithmic decision-making.
no code implementations • 31 Dec 2017 • Klaus Broelemann, Thomas Gottron, Gjergji Kasneci
Despite a multitude of algorithms to address the LTD problem that can be found in literature, only little is known about their overall performance with respect to effectiveness (in terms of truth discovery capabilities), efficiency and robustness.
1 code implementation • 13 Apr 2014 • Rene Pickhardt, Thomas Gottron, Martin Körner, Paul Georg Wagner, Till Speicher, Steffen Staab
In an extensive empirical experiment over English text corpora we demonstrate that our generalized language models lead to a substantial reduction of perplexity between 3. 1% and 12. 7% in comparison to traditional language models using modified Kneser-Ney smoothing.