  Entity linking is an important problem with many applications. Most previous
solutions were designed for settings where annotated training data is
available, which is, however, not the case in numerous domains. We propose a
light-weight and scalable entity linking method, Eigenthemes, that relies
solely on the availability of entity names and a referent knowledge base.
Eigenthemes exploits the fact that the entities that are truly mentioned in a
document (the "gold entities") tend to form a semantically dense subset of the
set of all candidate entities in the document. Geometrically speaking, when
representing entities as vectors via some given embedding, the gold entities
tend to lie in a low-rank subspace of the full embedding space. Eigenthemes
identifies this subspace using the singular value decomposition and scores
candidate entities according to their proximity to the subspace. On the
empirical front, we introduce multiple strong baselines that compare favorably
to (and sometimes even outperform) the existing state of the art. Extensive
experiments on benchmark datasets from a variety of real-world domains showcase
the effectiveness of our approach.
