Statistical modality tagging from rule-based annotations and crowdsourcing
This work addresses the challenge of sparse modality triggers in linguistic tagging, which is incremental in improving data collection methods for a specific NLP task.
The authors tackled the problem of training an automatic modality tagger by gathering training data through a high-recall rule-based tagger and crowdsourcing annotations via Mechanical Turk, resulting in a multi-class SVM tagger that delivers good performance.
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.