  Monitoring the biomedical literature for cases of Adverse Drug Reactions
(ADRs) is a critically important and time consuming task in pharmacovigilance.
The development of computer assisted approaches to aid this process in
different forms has been the subject of many recent works. One particular area
that has shown promise is the use of Deep Neural Networks, in particular,
Convolutional Neural Networks (CNNs), for the detection of ADR relevant
sentences. Using token-level convolutions and general purpose word embeddings,
this architecture has shown good performance relative to more traditional
models as well as Long Short Term Memory (LSTM) models. In this work, we
evaluate and compare two different CNN architectures using the ADE corpus. In
addition, we show that by de-duplicating the ADR relevant sentences, we can
greatly reduce overoptimism in the classification results. Finally, we evaluate
the use of word embeddings specifically developed for biomedical text and show
that they lead to a better performance in this task.
