Conditional random fields and support vector machines for disorder named entity recognition in clinical texts

Authors:
Dingcheng Li;Karin Kipper-Schuler;Guergana Savova
Affiliations:
University of Minnesota, Minneapolis, Minnesota;Mayo Clinic College of Medicine, Rochester, Minnesota;Mayo Clinic College of Medicine, Rochester, Minnesota
Venue:
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Year:
2008

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Abstract

We present a comparative study between two machine learning methods, Conditional Random Fields and Support Vector Machines for clinical named entity recognition. We explore their applicability to clinical domain. Evaluation against a set of gold standard named entities shows that CRFs outperform SVMs. The best F-score with CRFs is 0.86 and for the SVMs is 0.64 as compared to a baseline of 0.60.