We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.
@article{arxiv.2003.13032,
title = {Named Entities in Medical Case Reports: Corpus and Experiments},
author = {Sarah Schulz and Jurica Ševa and Samuel Rodriguez and Malte Ostendorff and Georg Rehm},
journal= {arXiv preprint arXiv:2003.13032},
year = {2020}
}
Comments
Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear