  Entity resolution seeks to merge databases as to remove duplicate entries
where unique identifiers are typically unknown. We review modern blocking
approaches for entity resolution, focusing on those based upon locality
sensitive hashing (LSH). First, we introduce $k$-means locality sensitive
hashing (KLSH), which is based upon the information retrieval literature and
clusters similar records into blocks using a vector-space representation and
projections. Second, we introduce a subquadratic variant of LSH to the
literature, known as Densified One Permutation Hashing (DOPH). Third, we
propose a weighted variant of DOPH. We illustrate each method on an application
to a subset of the ongoing Syrian conflict, giving a discussion of each method.
