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Entity disambiguation (ED) is the last step of entity linking (EL), whencandidate entities are reranked according to the context they appear in. Alldatasets for training and evaluating models for EL consist of conveniencesamples, such as news articles and tweets, that propagate the prior probabilitybias of the entity distribution towards more frequently occurring entities. Itwas previously shown that the performance of the EL systems on such datasets isoverestimated since it is possible to obtain higher accuracy scores by merelylearning the prior. To provide a more adequate evaluation benchmark, weintroduce the ShadowLink dataset, which includes 16K short text snippetsannotated with entity mentions. We evaluate and report the performance ofpopular EL systems on the ShadowLink benchmark. The results show a considerabledifference in accuracy between more and less common entities for all of the ELsystems under evaluation, demonstrating the effects of prior probability biasand entity overshadowing.

Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing