- Vinodkumar Prabhakaran, Michael Bloodgood, Mona T. Diab, B. Dorr, Lori S. Levin, C. Piatko, Owen Rambow, Benjamin Van Durme. 2012. from Rule-based Annotations and Crowdsourcing. Abstract: 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.
