A Hedgehop over a Max-Margin Framework Using Hedge Cues
Year: 2010
Type: conference-paper
Abstract: 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%.
Author: Maria Georgescul
Institution: University of Geneva
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W208016162