  We define a representation framework for extracting spatial information from
radiology reports (Rad-SpRL). We annotated a total of 2000 chest X-ray reports
with 4 spatial roles corresponding to the common radiology entities. Our focus
is on extracting detailed information of a radiologist's interpretation
containing a radiographic finding, its anatomical location, corresponding
probable diagnoses, as well as associated hedging terms. For this, we propose a
deep learning-based natural language processing (NLP) method involving both
word and character-level encodings. Specifically, we utilize a bidirectional
long short-term memory (Bi-LSTM) conditional random field (CRF) model for
extracting the spatial roles. The model achieved average F1 measures of 90.28
and 94.61 for extracting the Trajector and Landmark roles respectively whereas
the performance was moderate for Diagnosis and Hedge roles with average F1 of
71.47 and 73.27 respectively. The corpus will soon be made available upon
request.
