Rutabaga by any other name: extracting biological names
Journal of Biomedical Informatics - Special issue: Sublanguage
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Bio-medical entity extraction using Support Vector Machines
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Protein name tagging for biomedical annotation in text
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
SympGraph: a framework for mining clinical notes through symptom relation graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Due to the lack of annotated data sets, there are few studies on machine learning based approaches to extract named entities (NEs) in clinical text. The 2009 i2b2 NLP challenge is a task to extract six types of medication related NEs, including medication names, dosage, mode, frequency, duration, and reason from hospital discharge summaries. Several machine learning based systems have been developed and showed good performance in the challenge. Those systems often involve two steps: 1) recognition of medication related entities; and 2) determination of the relation between a medication name and its modifiers (e.g., dosage). A few machine learning algorithms including Conditional Random Field (CRF) and Maximum Entropy have been applied to the Named Entity Recognition (NER) task at the first step. In this study, we developed a Support Vector Machine (SVM) based method to recognize medication related entities. In addition, we systematically investigated various types of features for NER in clinical text. Evaluation on 268 manually annotated discharge summaries from i2b2 challenge showed that the SVM-based NER system achieved the best F-score of 90.05% (93.20% Precision, 87.12% Recall), when semantic features generated from a rule-based system were included.