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We explore training an automatic modality tagger. Modality is the attitudethat a speaker might have toward an event or state. One of the main hurdles fortraining a linguistic tagger is gathering training data. This is particularlyproblematic for training a tagger for modality because modality triggers aresparse for the overwhelming majority of sentences. We investigate an approachto automatically training a modality tagger where we first gathered sentencesbased on a high-recall simple rule-based modality tagger and then providedthese sentences to Mechanical Turk annotators for further annotation. We usedthe resulting set of training data to train a precise modality tagger using amulti-class SVM that delivers good performance.

Statistical modality tagging from rule-based annotations and crowdsourcing