  Vocabulary mismatch is a central problem in information retrieval (IR), i.e.,
the relevant documents may not contain the same (symbolic) terms of the query.
Recently, neural representations have shown great success in capturing semantic
relatedness, leading to new possibilities to alleviate the vocabulary mismatch
problem in IR. However, most existing efforts in this direction have been
devoted to the re-ranking stage. That is to leverage neural representations to
help re-rank a set of candidate documents, which are typically obtained from an
initial retrieval stage based on some symbolic index and search scheme (e.g.,
BM25 over the inverted index). This naturally raises a question: if the
relevant documents have not been found in the initial retrieval stage due to
vocabulary mismatch, there would be no chance to re-rank them to the top
positions later. Therefore, in this paper, we study the problem how to employ
neural representations to improve the recall of relevant documents in the
initial retrieval stage. Specifically, to meet the efficiency requirement of
the initial stage, we introduce a neural index for the neural representations
of documents, and propose two hybrid search schemes based on both neural and
symbolic indices, namely the parallel search scheme and the sequential search
scheme. Our experiments show that both hybrid index and search schemes can
improve the recall of the initial retrieval stage with small overhead.
