Biomedical named entity recognition using generalized expectation criteria

It is difficult to apply machine learning to a domain which is short of labeled training data, such as biomedical named entity recognition (NER) which remains a challenging task because of its extraordinary complex nomenclature. In this paper, we proposed a semi-supervised method which can train condition random field (CRF) models using generalized expectation (GE) criteria to solve biomedical named entity recognition problem. In the proposed method, instead of "instance'' labeling, the "feature'' labeling is applied to get the training data which can save lots of labeling time. Latent Dirichlet Allocation (LDA) model was involved to choose the features for labeling. Experiment results show that the proposed method can dramatically improve the performance of biomedical NER through incorporating unlabeled data by feature labeling.