  Information Extraction (IE) is the task of automatically extracting
structured information from unstructured/semi-structured machine-readable
documents. Among various IE tasks, extracting actionable intelligence from
ever-increasing amount of data depends critically upon Cross-Document
Coreference Resolution (CDCR) - the task of identifying entity mentions across
multiple documents that refer to the same underlying entity. Recently, document
datasets of the order of peta-/tera-bytes has raised many challenges for
performing effective CDCR such as scaling to large numbers of mentions and
limited representational power. The problem of analysing such datasets is
called "big data". The aim of this paper is to provide readers with an
understanding of the central concepts, subtasks, and the current
state-of-the-art in CDCR process. We provide assessment of existing
tools/techniques for CDCR subtasks and highlight big data challenges in each of
them to help readers identify important and outstanding issues for further
investigation. Finally, we provide concluding remarks and discuss possible
directions for future work.
