  Biomedical named entity recognition (NER) is a critial task that aims to
identify structured information in clinical text, which is often replete with
complex, technical terms and a high degree of variability. Accurate and
reliable NER can facilitate the extraction and analysis of important biomedical
information, which can be used to improve downstream applications including the
healthcare system. However, NER in the biomedical domain is challenging due to
limited data availability, as the high expertise, time, and expenses are
required to annotate its data. In this paper, by using the limited data, we
explore various extrinsic factors including the corpus annotation scheme, data
augmentation techniques, semi-supervised learning and Brill transformation, to
improve the performance of a NER model on a clinical text dataset (i2b2 2012,
\citet{sun-rumshisky-uzuner:2013}). Our experiments demonstrate that these
approaches can significantly improve the model's F1 score from original 73.74
to 77.55. Our findings suggest that considering different extrinsic factors and
combining these techniques is a promising approach for improving NER
performance in the biomedical domain where the size of data is limited.
