(Association for Computational Linguistics, 2012-07-13) Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona; Dorr, Bonnie; Levin, Lori; Piatko, Christine; Rambow, Owen; Van Durme, Benjamin
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.