A hedgehop over a max-margin framework using hedge cues
|A hedgehop over a max-margin framework using hedge cues|
|Published in||CoNLL-2010: Shared Task - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Shared Task|
|Keyword(s)||Unknown (Extra: Biomedical domain, Bioscopes, Classification results, Data sets, Discriminative approach, Discriminative learning, F-score, Input features, Lexical information, Test sets, Training data, Wikipedia, Websites, Fences)|
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A hedgehop over a max-margin framework using hedge cues is a 2010 journal article written in English by Georgescul M. and published in CoNLL-2010: Shared Task - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Shared Task.
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%.
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