Leveraging Dual Gloss Encoders in Chinese Biomedical Entity Linking

Abstract

Entity linking is the task of assigning a unique identity to named entities mentioned in a text, a sort of word sense disambiguation that focuses on automatically determining a pre-defined sense for a target entity to be disambiguated. This study proposes the DGE (Dual Gloss Encoders) model for Chinese entity linking in the biomedical domain. We separately model a dual encoder architecture, comprising a context-aware gloss encoder and a lexical gloss encoder, for contextualized embedding representations. Dual gloss encoders are then jointly optimized to assign the nearest gloss with the highest score for target entity disambiguation. The experimental datasets consist of a total of 10,218 sentences that were manually annotated with glosses defined in the BabelNet 5.0 across 40 distinct biomedical entities. Experimental results show that the DGE model achieved an F1-score of 97.81, outperforming other existing methods. A series of model analyses indicate that the proposed approach is effective for Chinese biomedical entity linking.