  State of the art approaches for (embedding based) unsupervised semantic
search exploits either compositional similarity (of a query and a passage) or
pair-wise word (or term) similarity (from the query and the passage). By
design, word based approaches do not incorporate similarity in the larger
context (query/passage), while compositional similarity based approaches are
usually unable to take advantage of the most important cues in the context. In
this paper we propose a new compositional similarity based approach, called
variable centroid vector (VCVB), that tries to address both of these
limitations. We also presents results using a different type of compositional
similarity based approach by exploiting universal sentence embedding. We
provide empirical evaluation on two different benchmarks.
