Named Entity Recognition from Biomedical Text Using SVM

Published 1 May 2011

Zhenfei Ju, Jian Wang, Fei Zhu

DOI10.1109/icbbe.2011.5779984

Citations100

Abstract

Nowadays biomedical research is developing rapidly. A large number of biomedical knowledge exists in the form of unstructured text documents in various files. Named Entity Recognition (NER) from biomedical text is one of the basic task s of biomedical text mining, of which purpose is to recognize the name of the specified type from the collection of biomedical text. NER result is usually the processing object of other text mining. NER from biological text is the foundation of bioinformatics research. At present, the best f-measure of biological named entity recognition system has reached more than 80%, but is lower than general NER system which can reach about 90%. Here we use support vector machine (SVM), which is an effective and efficient tool to analyze data and recognize patterns, to recognize biomedical named entity. We get data set from GENIA corpus which is a collection of Medline abstracts. In the experiment, we get precision rate= 84.24% and recall rate=80.76% finally.

Keywords

Computer ScienceBiochemistry, Genetics and Molecular Biology

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