A baseline approach for detecting sentences containing uncertainty

Authors:
Erik Tjong;Kim Sang
Affiliations:
University of Groningen;University of Groningen
Venue:
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Year:
2010

Citing 3
Cited 1

More accurate tests for the statistical significance of result differences

COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Exploring hedge identification in biomedical literature

Journal of Biomedical Informatics
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text

CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task

Cross-genre and cross-domain detection of semantic uncertainty

Computational Linguistics

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Abstract

We apply a baseline approach to the CoNLL-2010 shared task data sets on hedge detection. Weights have been assigned to cue words marked in the training data based on their occurrences in certain and uncertain sentences. New sentences received scores that correspond with those of their best scoring cue word, if present. The best acceptance scores for uncertain sentences were determined using 10-fold cross validation on the training data. This approach performed reasonably on the shared task's biological (F=82.0) and Wikipedia (F=62.8) data sets.