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The enormous growth of research publications has made it challenging foracademic search engines to bring the most relevant papers against the givensearch query. Numerous solutions have been proposed over the years to improvethe effectiveness of academic search, including exploiting query expansion andcitation analysis. Query expansion techniques mitigate the mismatch between thelanguage used in a query and indexed documents. However, these techniques cansuffer from introducing non-relevant information while expanding the originalquery. Recently, contextualized model BERT to document retrieval has been quitesuccessful in query expansion. Motivated by such issues and inspired by thesuccess of BERT, this paper proposes a novel approach called QeBERT. QeBERTexploits BERT-based embedding and Citation Network Analysis (CNA) in queryexpansion for improving scholarly search. Specifically, we use thecontext-aware BERT-embedding and CNA for query expansion in Pseudo-RelevanceFeedback (PRF) fash-ion. Initial experimental results on the ACL dataset showthat BERT-embedding can provide a valuable augmentation to query expansion andimprove search relevance when combined with CNA.

BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search