A maximum entropy approach to natural language processing
Computational Linguistics
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Cascaded classifiers for confidence-based chemical named entity recognition
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Reranking for biomedical named-entity recognition
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Two-phase biomedical named entity recognition using a hybrid method
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Two-Stage named-entity recognition using averaged perceptrons
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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Clinical named entities convey great deal of knowledge in clinical notes. This paper investigates named entity recognition from clinical notes using machine learning approaches. We present a cascading system that uses a Conditional Random Fields model, a Support Vector Machine and a Maximum Entropy to reclassify the identified entities in order to reduce misclassification. Voting strategy was employed to determine the class of the recognised entities between the three classifiers. The experiments were conducted on a corpus of 311 manually annotated admission summaries form an Intensive Care Unit. The recognition of 10 types of clinical named entities using 10 fold cross-validation achieved an overall results of 83.3 F-score. The reclassifier effectively increased the performance over stand-alone CRF models by 3.35 F-score.