Exploitation of Linguistic Features Using a CRF-Based Biomedical Named Entity Recognizer

As new high-throughput technologies have created an explosion of biomedical literature, there arises a pressing need for automatic information extraction from the literature bank. To this end, biomedical named entity recognition (NER) from natural language text is indispensable. Current NER approaches include: dictionary based, rule based, or machine learning based. Since there is no consolidated nomenclature for most biomedical NEs, any NER system relying on limited dictionaries or rules does not seem to perform satisfactorily. In this paper, we consider a machine learning model, CRF, for the construction of our NER framework, which is a well known model for solving other sequence tagging problems. In our framework, we fully utilize available resources including dictionaries and lexical analyzers, and represent them as linguistic features in the CRF model. In our experiment on the JNLPBA 2004 data, without any post-processing, our system achieves a satisfactory F-score of 69.7%.