  Search engines often follow a two-phase paradigm where in the first stage
(the retrieval stage) an initial set of documents is retrieved and in the
second stage (the re-ranking stage) the documents are re-ranked to obtain the
final result list. While deep neural networks were shown to improve the
performance of the re-ranking stage in previous works, there is little
literature about using deep neural networks to improve the retrieval stage. In
this paper, we study the merits of combining deep neural network models and
lexical models for the retrieval stage. A hybrid approach, which leverages both
semantic (deep neural network-based) and lexical (keyword matching-based)
retrieval models, is proposed. We perform an empirical study, using a publicly
available TREC collection, which demonstrates the effectiveness of our approach
and sheds light on the different characteristics of the semantic approach, the
lexical approach, and their combination.
