  The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and
assigning relevance scores for triples from type-like relations. Such scores
are a fundamental ingredient for ranking results in entity search. In this
paper, we propose a method that uses neural embedding techniques to accurately
calculate an entity score for a triple based on its nearest neighbor. We strive
to develop a new latent semantic model with a deep structure that captures the
semantic and syntactic relations between words. Our method has been ranked
among the top performers with accuracy - 0.74, average score difference - 1.74,
and average Kendall's Tau - 0.35.
