A hedgehop over a max-margin framework using hedge cues

Authors:
Maria Georgescul
Affiliations:
University of Geneva, Geneva
Venue:
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Year:
2010

Citing 8
Cited 5

Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
GridR: An R-Based Grid-Enabled Tool for Data Analysis in ACGT Clinico-Genomics Trials

E-SCIENCE '07 Proceedings of the Third IEEE International Conference on e-Science and Grid Computing
Exploring hedge identification in biomedical literature

Journal of Biomedical Informatics
Recognizing speculative language in biomedical research articles: a linguistically motivated perspective

BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Exploring ways beyond the simple supervised learning approach for biological event extraction

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Learning the scope of hedge cues in biomedical texts

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Finding hedges by chasing weasels: hedge detection using Wikipedia tags and shallow linguistic features

ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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

Multiple attribute frequent mining-based for dengue outbreak

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining uncertain sentences with multiple instance learning

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Modality and negation: An introduction to the special issue

Computational Linguistics
Cross-genre and cross-domain detection of semantic uncertainty

Computational Linguistics
Hedge detection as a lens on framing in the GMO debates: a position paper

ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

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%.