  In this paper, we introduce a new optimization approach to Entity Resolution.
Traditional approaches tackle entity resolution with hierarchical clustering,
which does not benefit from a formal optimization formulation. In contrast, we
model entity resolution as correlation-clustering, which we treat as a weighted
set-packing problem and write as an integer linear program (ILP). In this case
sources in the input data correspond to elements and entities in output data
correspond to sets/clusters. We tackle optimization of weighted set packing by
relaxing integrality in our ILP formulation. The set of potential sets/clusters
can not be explicitly enumerated, thus motivating optimization via column
generation. In addition to the novel formulation, we also introduce new dual
optimal inequalities (DOI), that we call flexible dual optimal inequalities,
which tightly lower-bound dual variables during optimization and accelerate
column generation. We apply our formulation to entity resolution (also called
de-duplication of records), and achieve state-of-the-art accuracy on two
popular benchmark datasets. The project page is available at the following url,
https://github.com/lokhande-vishnu/EntityResolution
