A Baseline Approach for Detecting Sentences Containing Uncertainty

We apply a baseline approach to the CoNLL-2010 shared task data sets on hedge detection. Weights have been assigned to cue words marked in the training data based on their occurrences in certain and uncertain sentences. New sentences received scores that correspond with those of their best scoring cue word, if present. The best acceptance scores for uncertain sentences were determined using 10-fold cross validation on the training data. This approach performed reasonably on the shared task's biological (F=82.0) and Wikipedia (F=62.8) data sets.