(ii) Context-sensitive disambiguation of morphological features (e.g., by combination with a chunker and adjustment of confidence scores for morphological features over all tokens in the current chunk, cf.

A novel feature of our approach as compared to existing applications of these methods is that confidence scores are not attached to plain strings, but to ontological descriptions: Tufis, for example, assigned confidence scores not to tools (as in a weighted majority vote), but rather, assessed the ‘credibility’ of a tool with respect to the predicted tag.

1: If a tool supports a description with its analysis, the confidence score is increased by l (or by l /n if the tool proposes n alternative annotations).

The hasCase descriptions have identical confidence scores , so that the first hasCase description that the algorithm encounters is chosen for the set of resulting descriptions, the other one is ruled out because of their inconsistency.

We use a confidence score to label only the instances that the classifiers are most certain about.

We compute a confidence score for instance 2' with respect to semantic class Ck, by considering both the score of the Ck, classifier as well as the scores of the competing classifiers.

After each training cycle, all of the classifiers are applied to the remaining unlabeled instances and each classifier labels the (positive) instances that it is most confident about (i.e., the instances that it classifies with a confidence score 2 60f).