Clustering-based Inference for Biomedical Entity Linking
This addresses entity linking in biomedical text, where mentions are often generic or specialized, by leveraging relationships between mentions, offering a domain-specific incremental improvement.
The paper tackles the problem of biomedical entity linking with limited labeled data by proposing a model that uses clustering to group mentions and make joint predictions, improving accuracy by 3.0 points over independent predictions and an additional 2.3 points with clustering-based inference.
Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points.