  Cross-document co-reference resolution (CDCR) is the task of identifying and
linking mentions to entities and concepts across many text documents. Current
state-of-the-art models for this task assume that all documents are of the same
type (e.g. news articles) or fall under the same theme. However, it is also
desirable to perform CDCR across different domains (type or theme). A
particular use case we focus on in this paper is the resolution of entities
mentioned across scientific work and newspaper articles that discuss them.
Identifying the same entities and corresponding concepts in both scientific
articles and news can help scientists understand how their work is represented
in mainstream media. We propose a new task and English language dataset for
cross-document cross-domain co-reference resolution (CD$^2$CR). The task aims
to identify links between entities across heterogeneous document types. We show
that in this cross-domain, cross-document setting, existing CDCR models do not
perform well and we provide a baseline model that outperforms current
state-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool and
guidelines as well as our model for cross-document cross-domain co-reference
are all supplied as open access open source resources.
