Journal article

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

P Brown, Y Zhou, AC Tan, MA El-Esawi, T Liehr, O Blanck, DP Gladue, GMF Almeida, T Cernava, CO Sorzano, AWK Yeung, MS Engel, AR Chandrasekaran, T Muth, MS Staege, SV Daulatabad, D Widera, J Zhang, A Meule, K Honjo Show all

Database | Published : 2019

Open access

Abstract

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contribut..

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