Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An integrated framework for de-identifying unstructured medical data
Data & Knowledge Engineering
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As Electronic Health Records are growing exponentially along with large quantities of unstructured clinical information that could be used for research purposes, protecting patient privacy becomes a challenge that needs to be met. In this paper, we present a novel hybrid system designed to improve the current strategies used for person names de-identification. To overcome this task, our system comprises several components designed to accomplish two separate goals: 1) achieve the highest recall (no patient data can be exposed); and 2) create methods to filter out false positives. As a result, our system reached 92.6% F2-measure when de-identifying person names in Veteran's Health Administration clinical notes, and considerably outperformed other existing "out-of-the-box" de-identification or named entity recognition systems.