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.
@article{arxiv.1804.08057,
title = {A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search},
author = {Md Faisal Mahbub Chowdhury and Vijil Chenthamarakshan and Rishav Chakravarti and Alfio M. Gliozzo},
journal= {arXiv preprint arXiv:1804.08057},
year = {2019}
}