\method: Improving Medical Entity Linking with Semantic Type Prediction
Table 5: Category distribution of different datasets. Overall, we find that our proposed datasets: WikiMed and PubMedDS give diverse coverage across all semantic types.
['[BOLD] Categories', '[BOLD] NCBI', '[BOLD] Bio CDR', '[BOLD] ShARe', '[BOLD] MedMentions', '[BOLD] WikiMed', '[BOLD] PubMedDS']
Wikipedia, though not a medical data source, does include many mentions of medical concepts that can inform entity typing models. In order to leverage transfer learning, an effective tool to solve the problem of insufficient training data in specialized domains transfer1; transfer2; transfer3, we developed a Wikipedia dataset for medical entity linking, which we use to pre-train \method before fine-tuning it on medical data. We utilize three knowledge sources to map hyperlinks in Wikipedia articles to UMLS entities: Wikidata wikidata, Freebase freebase, and the NCBI Taxonomy ncbi_mapping. This maps approximately 60,500 Wikipedia page IDs to UMLS concepts, with which we extract 652,000 relevant mentions from 6 million Wikipedia articles to create the WikiMed dataset. Since Wikipedia is human-curated, we assume that most of its linkages are of sufficiently high-quality to yield accurate mappings. Thus, we also utilize WikiMed as one of the evaluation datasets for assessing the performance of medical entity linkers on non-medical domain text. WikiMed is an order of magnitude larger in size than previous medical entity linking datasets such as MedMentions medmentions and the NCBI disease corpus ncbi_data, and covers medical entities from diverse semantic types.