  Multi-label sentences (text) in the clinical domain result from the rich
description of scenarios during patient care. The state-of-theart methods for
assertion detection mostly address this task in the setting of a single
assertion label per sentence (text). In addition, few rules based and deep
learning methods perform negation/assertion scope detection on single-label
text. It is a significant challenge extending these methods to address
multi-label sentences without diminishing performance. Therefore, we developed
a convolutional neural network (CNN) architecture to localize multiple labels
and their scopes in a single stage end-to-end fashion, and demonstrate that our
model performs atleast 12% better than the state-of-the-art on multi-label
clinical text.
