SpiNet - A FrameNet-like Schema for Automatic Information Extraction about Spine from Scientific Papers

New medical research concerning the spine and its diseases are incrementally made available through biomedical literature repositories. Several Natural Language Processing (NLP) tasks, like Semantic Role Labelling (SRL) and Information Extraction (IE), can offer support for, automatically, extracting relevant information about spine, from scientific papers. This paper presents a domain-specific FrameNet, called SpiNet, for automatic information extraction about spine concepts and their semantic types. For this, we use the frame semantic and the MeSH ontology in order to extract the relevant information about a disease, a treatment, a medication, a sign or symptom, related to spine medical domain. The differential of this work is the enrichment of SpiNet's base with the MeSH ontology, whose terms, concepts, descriptors and semantic types enable automatic semantic annotation. We use the SpiNet framework in order to annotate one hundred of scientific papers and the F1-score metric, calculated between the classification of relevant sentences performed by the system and the human physiotherapists, achieved the result of 0.83.