  Drug prescriptions are essential information that must be encoded in
electronic medical records. However, much of this information is hidden within
free-text reports. This is why the medication extraction task has emerged. To
date, most of the research effort has focused on small amount of data and has
only recently considered deep learning methods. In this paper, we present an
independent and comprehensive evaluation of state-of-the-art neural
architectures on the I2B2 medical prescription extraction task both in the
supervised and semi-supervised settings. The study shows the very competitive
performance of simple DNN models on the task as well as the high interest of
pre-trained models. Adapting the latter models on the I2B2 dataset enables to
push medication extraction performances above the state-of-the-art. Finally,
the study also confirms that semi-supervised techniques are promising to
leverage large amounts of unlabeled data in particular in low resource setting
when labeled data is too costly to acquire.
