WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document.
['Method', 'recall@64', 'recall@4', 'recall@1', 'N. recall@4', 'N. recall@1']
The performance of the candidate generation methods for word- In all top k positions, BM25 outperforms TFIDFchar, which is surprising given that TFIDFchar was reported to achieve strong performance for entity linking in murty-etal-2018-hierarchical.