Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Negation recognition in medical narrative reports
Information Retrieval
Inter-coder agreement for computational linguistics
Computational Linguistics
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Negation detection in Swedish clinical text
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
ESAIR '10 Proceedings of the third workshop on Exploiting semantic annotations in information retrieval
Are you sure that this happened? assessing the factuality degree of events in text
Computational Linguistics
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In this paper we describe the creation of a consensus corpus that was obtained through combining three individual annotations of the same clinical corpus in Swedish. We used a few basic rules that were executed automatically to create the consensus. The corpus contains negation words, speculative words, uncertain expressions and certain expressions. We evaluated the consensus using it for negation and speculation cue detection. We used Stanford NER, which is based on the machine learning algorithm Conditional Random Fields for the training and detection. For comparison we also used the clinical part of the BioScope Corpus and trained it with Stanford NER. For our clinical consensus corpus in Swedish we obtained a precision of 87.9 percent and a recall of 91.7 percent for negation cues, and for English with the Bioscope Corpus we obtained a precision of 97.6 percent and a recall of 96.7 percent for negation cues.