Statistical modality tagging from rule-based annotations and crowdsourcing

Authors:
Vinodkumar Prabhakaran;Michael Bloodgood;Mona Diab;Bonnie Dorr;Lori Levin;Christine D. Piatko;Owen Rambow;Benjamin Van Durme
Affiliations:
Columbia University;University of Maryland;Columbia University;University of Maryland;Carnegie Mellon University;Johns Hopkins University;Columbia University;Johns Hopkins University
Venue:
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
Year:
2012

Citing 12
Cited 0

An algorithm for suffix stripping

Readings in information retrieval
Making large-scale support vector machine learning practical

Advances in kernel methods
Mood and modality: out of theory and into the fray

Natural Language Engineering
Modals as a problem for MT

COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Fast methods for kernel-based text analysis

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning the scope of hedge cues in biomedical texts

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
TEXT2TABLE: medical text summarization system based on named entity recognition and modality identification

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Semantic inference at the lexical-syntactic level

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Committed belief annotation and tagging

ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
Automatic committed belief tagging

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic extraction of lexico-syntactic patterns for detection of negation and speculation scopes

HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Modality and negation in simt use of modality and negation in semantically-informed syntactic mt

Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

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.