Meta-Learning with Selective Data Augmentation for Medical Entity Recognition

With the increasing number of annotated corpora for supervised Named Entity Recognition, it becomes interesting to study the combination and augmentation of these corpora for the same annotation task. In this paper, we particularly study the combination of heterogeneous corpora for Medical Entity Recognition by using a meta-learning classifier that combines the results of individual Conditional Random Fields (CRFs) models trained on different corpora. We propose selective data augmentation approaches and compare them with several metalearning algorithms and baselines. We evaluate our approach using four sub-classifiers trained on four heterogeneous corpora. We show that despite the high disagreements between the individual models on the four test corpora, our selective data augmentation approach improves performance on all test corpora and outperforms the combination of all training corpora.