Recognizing Biomedical Named Entities Using SVMs: Improving Recognition Performance with a Minimal Set of Features

In this paper, Support Vector Machines (SVMs) are applied to the identification and automatic annotation of biomedical named entities in the domain of molecular biology, as an extension of the traditional named entity recognition task to special domains. The effect of the use of well-known features such as word formation patterns, lexical, morphological, and surface words on recognition performance is investigated. Experiments have been conducted using the train and test data made public at the Bio-Entity Recognition Task at JNLPBA 2004. An F-score of 69.87% was obtained by using a carefully selected combination of a minimal set of features, which can be easily computed from training data without any use of post-processing or external resources.