Abstract
Negative medical findings are prevalent in
clinical reports, yet discriminating them from
positive findings remains a challenging task
for information extraction. Most of the existing systems treat this task as a pipeline of
two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem
and present a novel end-to-end neural model to
jointly extract entities and negations. We extend a standard hierarchical encoder-decoder
NER model and first adopt a shared encoder
followed by separate decoders for the two
tasks. This architecture performs considerably
better than the previous rule-based and machine learning-based systems. To overcome
the problem of increased parameter size especially for low-resource settings, we propose
the Conditional Softmax Shared Decoder architecture which achieves state-of-art results
for NER and negation detection on the 2010
i2b2/VA challenge dataset and a proprietary
de-identified clinical dataset.