Statistical modality tagging 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.
Cite
@article{arxiv.1503.01190,
title = {Statistical modality tagging from rule-based annotations and crowdsourcing},
author = {Vinodkumar Prabhakaran and Michael Bloodgood and Mona Diab and Bonnie Dorr and Lori Levin and Christine D. Piatko and Owen Rambow and Benjamin Van Durme},
journal= {arXiv preprint arXiv:1503.01190},
year = {2016}
}
Comments
8 pages, 6 tables; appeared in Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, July 2012; In Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pages 57-64, Jeju, Republic of Korea, July 2012. Association for Computational Linguistics