Paper: A Hedgehop over a Max-Margin Framework Using Hedge Cues

ACL ID W10-3004
Title A Hedgehop over a Max-Margin Framework Using Hedge Cues
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

In this paper, we describe the experimental settings we adopted in the context of the 2010 CoNLL shared task for detecting sentences containing uncertainty. The classification results reported on are obtained using discriminative learning with features essentially incorporating lexical information. Hyper-parameters are tuned for each domain: using BioScope training data for the biomedical domain and Wikipedia training data for the Wikipedia test set. By allowing an efficient handling of combinations of large-scale input features, the discriminative approach we adopted showed highly competitive empirical results for hedge detection on the Wikipedia dataset: our system is ranked as the first with an F-score of 60.17%.