You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews [chapter]

Harrisen Scells, Guido Zuccon, Bevan Koopman
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Coordination level matching is a ranking method originally proposed to rank documents given Boolean queries that is now several decades old. Rank fusion is a relatively recent method for combining runs from multiple systems into a single ranking, and has been shown to significantly improve the ranking. This paper presents a novel extension to coordination level matching, by applying rank fusion to each sub-clause of a Boolean query. We show that, for the tasks of systematic review screening
more &raquo; ... ritisation and stopping estimation, our method significantly outperforms the state-of-the-art learning to rank and bag-ofwords-based systems for this domain. Our fully automatic, unsupervised method has (i) the potential for significant real-world cost savings (ii) does not rely on any intervention from the user, and (iii) is significantly better at ranking documents given only a Boolean query in the context of systematic reviews when compared to other approaches.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-45439-5_27">doi:10.1007/978-3-030-45439-5_27</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ntf26fzew5g2vnwqav6y2feaeq">fatcat:ntf26fzew5g2vnwqav6y2feaeq</a> </span>
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