Traditional approaches to combining classifiers attempt to improve classification accuracy at the cost of increased processing. They may be viewed as providing an accuracy-speed tr...
Kumar Chellapilla, Michael Shilman, Patrice Simard
In this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to Basque languagel. The methods we have used in disambiguation...
In this paper, we propose a linear model-based general framework to combine k-best parse outputs from multiple parsers. The proposed framework leverages on the strengths of previo...
Recently system combination has been shown to be an effective way to improve translation quality over single machine translation systems. In this paper, we present a simple and ef...
In emotion recognition, a widely-used method to reconciliate disagreement between multiple human evaluators is to perform majority-voting on their assigned class labels. Instead, ...