A Machine Learning Approach for Phenotype Name Recognition

Extracting biomedical named entities is one of the major challenges in automatic processing of biomedical literature. This paper proposes a machine learning approach for finding phenotype names in text. Features are included in a machine learning infrastructure to implement the rules found in our previously developed rule-based system. The system also uses two available resources: MetaMap and HPO. As we are not aware of any available corpus for phenotype names, a corpus has been constructed. Since manual tagging of the corpus was not possible for us, we started tagging only HPO phenotypes in the corpus and then using a semi-supervised learning method, the tagging process improved. The evaluation results (F-Score 92.25) suggest that the system achieved good performance and it outperforms the rule-based system.