  The enormous growth of research publications has made it challenging for
academic search engines to bring the most relevant papers against the given
search query. Numerous solutions have been proposed over the years to improve
the effectiveness of academic search, including exploiting query expansion and
citation analysis. Query expansion techniques mitigate the mismatch between the
language used in a query and indexed documents. However, these techniques can
suffer from introducing non-relevant information while expanding the original
query. Recently, contextualized model BERT to document retrieval has been quite
successful in query expansion. Motivated by such issues and inspired by the
success of BERT, this paper proposes a novel approach called QeBERT. QeBERT
exploits BERT-based embedding and Citation Network Analysis (CNA) in query
expansion for improving scholarly search. Specifically, we use the
context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance
Feedback (PRF) fash-ion. Initial experimental results on the ACL dataset show
that BERT-embedding can provide a valuable augmentation to query expansion and
improve search relevance when combined with CNA.
