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

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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.

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APA

Li, D., Kipper-Schuler, K., & Savova, G. (2008). Conditional random fields and support vector machines for disorder named entity recognition in clinical texts. In BioNLP 2008 - Current Trends in Biomedical Natural Language Processing, Proceedings of the Workshop (pp. 94–95). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1572306.1572326

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