Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Role of local context in automatic deidentification of ungrammatical, fragmented text
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Determining Modality and Factuality for Text Entailment
ICSC '07 Proceedings of the International Conference on Semantic Computing
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Annotating attribution in the Penn Discourse TreeBank
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
HedgeHunter: a system for hedge detection and uncertainty classification
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Learning local content shift detectors from document-level information
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2nd international workshop on Managing interoperability and compleXity in health systems
Statistical modality tagging from rule-based annotations and crowdsourcing
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
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With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. It is not, however, easy to extract information because these reports are written in natural language. To address this problem, this paper presents a system that converts a medical text into a table structure. This system's core technologies are (1) medical event recognition modules and (2) a negative event identification module that judges whether an event actually occurred or not. Regarding the latter module, this paper also proposes an SVM-based classifier using syntactic information. Experimental results demonstrate empirically that syntactic information can contribute to the method's accuracy.