Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploring hedge identification in biomedical literature
Journal of Biomedical Informatics
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
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
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
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In this paper, we proposed a hedge detection method with average perceptron, which was used in the closed challenge in CoNLL-2010 Shared Task. There are two subtasks: (1) detecting uncertain sentences and (2) identifying the in-sentence scopes of hedge cues. We use the unified learning algorithm for both subtasks since that the hedge score of sentence can be decomposed into scores of the words, especially the hedge words. On the biomedical corpus, our methods achieved F-measure with 77.86% in detecting in-domain uncertain sentences, 77.44% in recognizing hedge cues, and 19.27% in identifying the scopes.