id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020932-o5scqiyk	Zhong, Wei	Accelerating Substructure Similarity Search for Formula Retrieval	2020-03-17		.txt	text/plain	4602	278	65	In text similarity search, query processing can be accelerated through dynamic pruning [18] , which typically estimates score upperbounds to prune documents unlikely to be in the top K results. As a result, the posting list entry also stores the root node ID for indexed paths, in order to reconstruct matches substructures at merge time. Define partial upperbound matrix W = {w i,j } |Tq|×|T| where T = {T(m), m ∈ T q } are all the token paths from query OPT (T is essentially the same as tokenized P(T q )), and a binary variable x |T|×1 indicating which corresponding posting lists are placed in the non-requirement set. We have presented rank-safe dynamic pruning strategies that produce an upperbound estimation of structural similarity in order to speedup formula search using subtree matching. Our dynamic pruning strategies and specialized inverted index are different from traditional linear text search pruning methods and they further associate query structure representation with posting lists.	./cache/cord-020932-o5scqiyk.txt	./txt/cord-020932-o5scqiyk.txt