The impact of near domain transfer on biomedical named entity recognition

Current research in fully supervised biomedical named entity recognition (bioNER) is often conducted in a setting of low sample sizes. Whilst experimental results show strong performance in-domain it has been recognised that quality suffers when models are applied to heterogeneous text collections. However the causal factors have until now been uncertain. In this paper we describe a controlled experiment into near domain bias for two Medline corpora on hereditary diseases. Five strategies are employed for mitigating the impact of near domain transference including simple transference, pooling, stacking, class re-labeling and feature augmentation. We measure their effect on f-score performance against an in domain baseline. Stacking and feature augmentation mitigate f-score loss but do not necessarily result in superior performance except for selected classes. Simple pooling of data across domains failed to exploit size effects for most classes. We conclude that we can expect lower performance and higher annotation costs if we do not adequately compensate for the distributional dissimilarities of domains during learning.