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Cross-document co-reference resolution (CDCR) is the task of identifying andlinking mentions to entities and concepts across many text documents. Currentstate-of-the-art models for this task assume that all documents are of the sametype (e.g. news articles) or fall under the same theme. However, it is alsodesirable to perform CDCR across different domains (type or theme). Aparticular use case we focus on in this paper is the resolution of entitiesmentioned across scientific work and newspaper articles that discuss them.Identifying the same entities and corresponding concepts in both scientificarticles and news can help scientists understand how their work is representedin mainstream media. We propose a new task and English language dataset forcross-document cross-domain co-reference resolution (CD$^2$CR). The task aimsto identify links between entities across heterogeneous document types. We showthat in this cross-domain, cross-document setting, existing CDCR models do notperform well and we provide a baseline model that outperforms currentstate-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool andguidelines as well as our model for cross-document cross-domain co-referenceare all supplied as open access open source resources.

CD2CR: Co-reference Resolution Across Documents and Domains