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Two classifiers -- Support Vector Machine (SVM) and Conditional Random Fields (CRFs) are applied here for the recognition of biomedical named entities. According to their different characteristics, the results of two classifiers are merged to achieve better performance. We propose an automatic corpus expansion method for SVM and CRF to overcome the shortage of the annotated training data. In addition, we incorporate a keyword-based post-processing step to deal with the remaining problems such as assigning an appropriate named entity tag to the word/phrase containing parentheses.

POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004