Background Emergency room reports are a specific kind of text, posing specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries vs. non-violence-related injuries, is thus a relevant task, to the ends of devising alerting mechanisms to track violence episodes.
Methods We present a system to detect episodes of violence from the textual descriptions contained in emergency room reports. It employs a deep neural network for categorizing textual ER reports data. Additionally, the system complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of the terms herein, along with syntactic and semantic information.
Results We tested our system on a set of real data of emergency room reports, coming from an Italian branch of the EU-Injury Database (EU-IDB) project, annotated by hospital staff. Our experimentation shows that the system produces accurate categorization (of violent vs. non violent records), paired with interesting results on the explanation of such output. At times, it also allowed unveiling annotation errors committed by hospital staff.
Conclusions In the last few years deep architectures and word embeddings have been compared to a tsunami hitting AI and the area concerned with natural language processing. Only at a later time we have been realizing that the stunning output of deep networks needed to be explained: our proposal, combining distributed and symbolic (frame-like) representations are a possible answer to this pressing request for interpretability. Although the present application is focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.
XAI, explanation, text categorization, categorization explanation, word embeddings, semantic frames, slot filling, event extraction, violent event tracking