  We explore training an automatic modality tagger. Modality is the attitude
that a speaker might have toward an event or state. One of the main hurdles for
training a linguistic tagger is gathering training data. This is particularly
problematic for training a tagger for modality because modality triggers are
sparse for the overwhelming majority of sentences. We investigate an approach
to automatically training a modality tagger where we first gathered sentences
based on a high-recall simple rule-based modality tagger and then provided
these sentences to Mechanical Turk annotators for further annotation. We used
the resulting set of training data to train a precise modality tagger using a
multi-class SVM that delivers good performance.
